google speech services

Google’s text-to-speech engine is getting new voices across Android apps

New voice models are being introduced for ‘clearer, more natural voices.’ that’s not exactly what we heard on our first listen..

By Jess Weatherbed , a news writer focused on creative industries, computing, and internet culture. Jess started her career at TechRadar, covering news and hardware reviews.

Share this story

If you buy something from a Verge link, Vox Media may earn a commission. See our ethics statement.

The speech engine Speech Services by Google is being upgraded to improve clarity and make text-to-speech voices in Android apps sound more natural. You can hear the difference between the old voices and the updated voices for yourself through prerecorded snippets on the Android Developers Blog .

Frankly, while the voices do sound clearer, I’m skeptical about the claims it sounds more natural. It’s also still difficult to ascertain what the first sentence in these US English-language recordings actually says — is this my gun? Is this my god? Apparently it says “is this mic on?” but that was lost on me.

All 421 voices in 67 languages within Google’s speech engine are getting a new voice model

All 421 voices in 67 languages within the system are getting a new voice model and synthesizer. The current default voice in “English-US” is changing to one built using “fresher speaker data,” which, alongside other updates, results in a recognizable improvement from the previous default voice. You can also listen to how the updated voices sound in languages such as “Spanish-US” and “Brazilian-Portuguese.”

The update announcement says that folks already using text-to-speech tech don’t need to do anything to receive the new voices, as “everything will happen behind the scenes” with the updates being downloaded automatically. Its listing on the Google Play Store states that the service is already used by a variety of native applications such as Google Maps, Google Translate, and the Android Recorder app, so chances are that if you use an Android device, you probably already use the Speech Services by Google speech engine, even if you don’t know it. The update is rolling out over the next few weeks to all 64-bit Android devices via the Google Play Store.

Ryzen CPU owners can now download better gaming performance thanks to a Windows 11 update

Brilliant is back from the brink, apple’s iphone 16 launch event is set for september, spacex falcon 9 booster ‘tipped over’ into the ocean during landing, martin shkreli must surrender his wu-tang album copies.

More from Google

Android 15’s first developer preview has arrived

The OnePlus 12R is a $500 phone with flagship tendencies

Google offers non-Pixel owners a way to avoid waiting on hold with latest test

How to make the most of Google Keep

28 September 2022

Listen to our major Text to Speech upgrades for 64 bit devices.

Posted by  Rakesh Iyer, Staff Software Engineer and  Leland Rechis, Group Product Manager

We are upgrading the Speech Services by Google speech engine in a big way, providing clearer, more natural voices. All 421 voices in 67 languages have been upgraded with a new voice model and synthesizer.

If you already use TTS and the Speech Services by Google engine, there is nothing to do – everything will happen behind the scenes as your users will have automatically downloaded the latest update. We’ve seen a significant side by side quality increase with this change, particularly in respects to clarity and naturalness.

With this upgrade we will also be changing the default voice in en-US to one that is built using fresher speaker data, which alongside our new stack, results in a drastic improvement. If your users have not selected a system voice, and you rely on system defaults, they will hear a slightly different speaker. You can hear the difference below

Speaker change and upgrade for EN-US

Speaker upgrades in a few other languages

This update will be rolling out to all 64 bit Android devices via the Google Play Store over the next few weeks as a part of the Speech Services by Google apk. If you are concerned your users have not updated this yet, you can check for the minimum version code ,210390644 on the package com.google.android.tts.

If you haven't used TTS in your projects yet, or haven’t given your users the ability to choose a voice within your app, it's fairly straightforward, and easy to experiment with. We’ve included some sample code to get you started. 

Here’s an example of how to set up voice synthesis, get a list of voices, and set a specific voice. We finally send a simple utterance to the synthesizer.

Google developers blog

• Help Center
• Google Assistant
• Privacy Policy
• Terms of Service
• Submit feedback

Learn how Google improves speech models

Many Google products involve speech recognition. For example, Google Assistant allows you to ask for help by voice, Gboard lets you dictate messages to your friends, and Google Meet provides auto captioning for your meetings.

Speech technologies increasingly rely on deep neural networks, a type of machine learning that helps us build more accurate and faster speech recognition models. Generally deep neural networks need larger amounts of data to work well and improve over time. This process of improvement is called model training.

What technologies we use to train speech models

Google’s speech team uses 3 broad classes of technologies to train speech models: conventional learning, federated learning, and ephemeral learning. Depending on the task and situation, some of these are more effective than others, and in some cases, we use a combination of them. This allows us to achieve the best quality possible, while providing privacy by design.

Conventional learning is how most of our speech models are trained.

How conventional learning works to train speech models

• With your explicit consent, audio samples are collected and stored on Google’s servers.
• A portion of these audio samples are annotated by human reviewers.
• In supervised training: Models are trained to mimic annotations from human reviewers for the same audio.
• In unsupervised training: Machine annotations are used instead of human annotations.

When training on equal amounts of data, supervised training typically results in better speech recognition models than unsupervised training because the annotations are higher quality. On the other hand, unsupervised training can learn from more audio samples since it learns from machine annotations, which are easier to produce.

How your data stays private

Learn more about how Google keeps your data private .

Federated learning is a privacy preserving technique developed at Google to train AI models directly on your phone or other device. We use federated learning to train a speech model when the model runs on your device and data is available for the model to learn from.

How federated learning works to train speech models

With federated learning, we train speech models without sending your audio data to Google’s servers.

• To enable federated learning, we save your audio data on your device.
• A training algorithm learns from this data on your device.
• A new speech model is formed by combining the aggregated learnings from your device along with learnings from all other participating devices.

How ephemeral learning works to train speech models

• As our systems convert incoming audio samples into text, those samples are sent to short-term memory (RAM).
• While the data is in RAM, a training algorithm learns from those audio data samples in real time.
• These audio data samples are deleted from short-term memory within minutes.

With ephemeral learning, your audio data samples are:

• Only held in short-term memory (RAM) and for no more than a few minutes.
• Never accessible by a human.
• Never stored on a server.
• Used to train models without any additional data that can identify you.

How Google will use & invest in these technologies

We’ll continue to use all 3 technologies, often in combination for higher quality. We’re also actively working to improve both federated and ephemeral learning for speech technologies. Our goal is to make them more effective and useful, and in ways that preserve privacy by default.

Need more help?

Try these next steps:.

Android apps are getting a ‘major’ Google TTS quality upgrade

“Speech Services by Google” is responsible for providing text-to-speech (TTS) and speech-to-text (transcription) capabilities for Android apps. Google is now rolling out a major TTS audio quality upgrade for 64-bit Android devices.

Android text-to-speech is getting “clearer, more natural voices” with a “significant side by side quality increase” touted. A new voice model and synthesizer for 64-bit devices is responsible for the improvement. 

All of Google’s 421 voices across 67 languages have been upgraded. EN-US in particular also benefits from a new default voice that’s “built using fresher speaker data.” Google is also advertising another “drastic improvement” in combination with the main TTS upgrade.

Developers that already use Android TTS and the Speech Services by Google engine don’t have to do anything to get the upgrade as “everything will happen behind the scenes as your users will have automatically downloaded the latest update.”

This update will be rolling out to all 64 bit Android devices via the Google Play Store over the next few weeks as a part of the Speech Services by Google apk. If you are concerned your users have not updated this yet, you can check for the minimum version code ,210390644 on the package com.google.android.tts.

According to the Play Store listing , Speech Services TTS is leveraged by:

• Google Play Books to “Read Aloud” your favorite book
• Google Translate to speak translations aloud so you can hear the pronunciation of a word
• Talkback and accessibility applications for spoken feedback across your device
• …and many other applications in Play Store

More on Android:

• You can test Android 13’s predictive back gesture with the Pixel Phone app today
• Most Google apps don’t actually support Android 13’s themed icons outside of Pixel phones
• Google previews Android’s new emoji ahead of December launch, releases animated collection
• These Samsung devices are reportedly getting Android 13 by the end of the year

FTC: We use income earning auto affiliate links. More.

Check out 9to5Google on YouTube for more news:

Breaking news for Android. Get the latest on app…

Editor-in-chief. Interested in the minutiae of Google and Alphabet. Tips/talk: [email protected]

Manage push notifications

• {{link.text}}

Speech Processing

Our goal in Speech Technology Research is twofold: to make speaking to devices around you (home, in car), devices you wear (watch), devices with you (phone, tablet) ubiquitous and seamless.

Our research focuses on what makes Google unique: computing scale and data. Using large scale computing resources pushes us to rethink the architecture and algorithms of speech recognition, and experiment with the kind of methods that have in the past been considered prohibitively expensive. We also look at parallelism and cluster computing in a new light to change the way experiments are run, algorithms are developed and research is conducted. The field of speech recognition is data-hungry, and using more and more data to tackle a problem tends to help performance but poses new challenges: how do you deal with data overload? How do you leverage unsupervised and semi-supervised techniques at scale? Which class of algorithms merely compensate for lack of data and which scale well with the task at hand? Increasingly, we find that the answers to these questions are surprising, and steer the whole field into directions that would never have been considered, were it not for the availability of significantly higher orders of magnitude of data.

We are also in a unique position to deliver very user-centric research. Researchers have the wealth of millions of users talking to Voice Search or the Android Voice Input every day. and can conduct live experiments to test and benchmark new algorithms directly in a realistic controlled environment. Whether these are algorithmic performance improvements or user experience and human-computer interaction studies, we keep our users very close to make sure we solve real problems and have real impact.

We have a huge commitment to the diversity of our users, and have made it a priority to deliver the best performance to every language on the planet. We currently have systems operating in more than 55 languages and we keep expanding our reach to more and more users. The challenges of internationalizing at scale is immense and rewarding. Many speakers of the languages we reach never had the experience of speaking to a computer before, and breaking this new ground brings up new research on how to better serve this wide variety of users. Combined with the unprecedented translation capabilities of Google Translate, we are now at the forefront of research in speech-to-speech translation and one step closer to a universal translator.

In terms of a challenge, indexing and transcribing the web’s audio content is another challenge we have set for ourself, and is nothing short of gargantuan, both in scope and difficulty. The videos uploaded every day on YouTube range from lectures, to newscasts, music videos and of course... cat videos. Making sense of them takes the challenges of noise robustness, music recognition, speaker segmentation, language detection to new levels of difficulty. The payoff is immense: imagine making every lecture on the web accessible to every language; this is the kind of impact we are striving for.

264 Publications

(Almost) Zero-Shot Cross-Lingual Spoken Language Understanding

Shyam Upadhyay, Manaal Faruqui , Gokhan Tur , Dilek Hakkani-Tur , Larry Heck

Proceedings of the IEEE ICASSP (2018)

An Analysis of Incorporating an External Language Model into a Sequence-to-Sequence Model

Anjuli Kannan , Yonnghui Wu , Patrick Nguyen, Tara N. Sainath , Zhifeng Chen , Rohit Prabhavalkar

ICASSP (2018)

Decoding the auditory brain with canonical component analysis

Alain de Cheveigné, Daniel D. E. Wong, Giovanni M. Di Liberto, Jens Hjortkjaer, Malcolm Slaney , Edmund Lalor

NeuroImage (2018)

Minimum Word Error Rate Training for Attention-based Sequence-to-Sequence Models

Rohit Prabhavalkar , Tara Sainath , Yonghui Wu , Patrick Nguyen, Zhifeng Chen , Chung-Cheng Chiu , Anjuli Kannan

ICASSP 2018 (to appear)

Multilingual Speech Recognition with a Single End-to-End Model

Shubham Toshniwal, Tara N. Sainath , Ron Weiss , Bo Li , Pedro Moreno , Eugene Weinsten , Kanishka Rao

ON USING BACKPROPAGATION FOR SPEECH TEXTURE GENERATION AND VOICE CONVERSION

Jan Chorowski, Ron J. Weiss , Rif A. Saurous , Samy Bengio

Sound source separation using phase difference and reliable mask selection

Chanwoo Kim , Anjali Menon, Michiel Bacchiani , Richard M. Stern

ICASSP (2018) (to appear)

Spectral distortion model for training phase-sensitive deep-neural networks for far-field speech recognition

Chanwoo Kim , Tara Sainath , Arun Narayanan , Ananya Misra , Rajeev Nongpiur, Michiel Bacchiani

ICASSP 2018 (2018)

State-of-the-art Speech Recognition With Sequence-to-Sequence Models

Chung-Cheng Chiu , Tara Sainath , Yonghui Wu , Rohit Prabhavalkar , Patrick Nguyen, Zhifeng Chen , Anjuli Kannan , Ron J. Weiss , Kanishka Rao , Katya Gonina, Navdeep Jaitly, Bo Li , Jan Chorowski, Michiel Bacchiani

A Cascade Architecture for Keyword Spotting on Mobile Devices

Alexander Gruenstein , Raziel Alvarez , Chris Thornton, Mohammadali Ghodrat

31st Conference on Neural Information Processing Systems (NIPS 2017), Long Beach, CA, USA (2017)

A Comparison of Sequence-to-Sequence Models for Speech Recognition

Rohit Prabhavalkar , Kanishka Rao , Tara Sainath , Bo Li , Leif Johnson , Navdeep Jaitly

Interspeech 2017, ISCA (2017)

A Segmental Framework for Fully-Unsupervised Large-Vocabulary Speech Recognition

Herman Kamper, Aren Jansen , Sharon Goldwater

Computer Speech and Language (2017) (to appear)

A more general method for pronunciation learning

Antoine Bruguier , Dan Gnanapragasam , Francoise Beaufays , Kanishka Rao , Leif Johnson

Interspeech 2017 (2017)

Acoustic Modeling for Google Home

Bo Li , Tara Sainath , Arun Narayanan , Joe Caroselli, Michiel Bacchiani , Ananya Misra , Izhak Shafran , Hasim Sak , Golan Pundak , Kean Chin, Khe Chai Sim, Ron J. Weiss , Kevin Wilson , Ehsan Variani , Chanwoo Kim , Olivier Siohan , Mitchel Weintraub, Erik McDermott , Rick Rose , Matt Shannon

INTERSPEECH 2017 (2017)

An Analysis of "Attention" in Sequence-to-Sequence Models

Rohit Prabhavalkar , Tara Sainath , Bo Li , Kanishka Rao , Navdeep Jaitly

Approaches for Neural-Network Language Model Adaptation

Fadi Biadsy , Michael Alexander Nirschl , Min Ma, Shankar Kumar

Interspeech 2017, Stockholm, Sweden (2017)

Areal and Phylogenetic Features for Multilingual Speech Synthesis

Alexander Gutkin , Richard Sproat

Proc. of Interspeech 2017, ISCA, August 20–24, 2017, Stockholm, Sweden, pp. 2078-2082

Attention-Based Models for Text-Dependent Speaker Verification

F A Rezaur Rahman Chowdhury, Quan Wang , Ignacio Lopez Moreno , Li Wan

Binaural processing for robust speech recognition of degraded speech

Anjali Menon, Chanwoo Kim , Umpei Kurokawa, Richard M. Stern

IEEE Automatic Speech Recognition and Understanding Workshop (2017)

Effectively Building Tera Scale MaxEnt Language Models Incorporating Non-Linguistic Signals

Fadi Biadsy , Mohammadreza Ghodsi , Diamantino Caseiro

Interpspeech 2017 (2017)

Efficient Implementation of the Room Simulator for Training Deep Neural Network Acoustic Models

Chanwoo Kim , Ehsan Variani , Arun Narayanan , Michiel Bacchiani

arxiv (2017)

End-to-End Training of Acoustic Models for Large Vocabulary Continuous Speech Recognition with TensorFlow

Ehsan Variani , Tom Bagby, Erik McDermott , Michiel Bacchiani

Endpoint detection using grid long short-term memory networks for streaming speech recognition

Bo Li , Carolina Parada , Gabor Simko , Shuo-yiin Chang , Tara Sainath

In Proc. Interspeech 2017 (to appear)

Generalized End-to-End Loss for Speaker Verification

Li Wan , Quan Wang , Alan Papir , Ignacio Lopez Moreno

Generation of large-scale simulated utterances in virtual rooms to train deep-neural networks for far-field speech recognition in Google Home

Chanwoo Kim , Ananya Misra , Kean Chin, Thad Hughes , Arun Narayanan , Tara Sainath , Michiel Bacchiani

interspeech 2017 (2017), pp. 379-383

Generative Model-Based Text-to-Speech Synthesis

Google's next-generation real-time unit-selection synthesizer using sequence-to-sequence LSTM-based autoencoders

Vincent Wan , Yannis Agiomyrgiannakis , Hanna Silen, Jakub Vit

Interspeech (2017)

Highway-LSTM and Recurrent Highway Networks for Speech Recognition

Golan Pundak , Tara Sainath

Proc. Interspeech 2017, ISCA

Human and Machine Hearing: Extracting Meaning from Sound

Richard F. Lyon

Cambridge University Press (2017)

Improved end-of-query detection for streaming speech recognition

Carolina Parada , Gabor Simko , Matt Shannon, Shuo-yiin Chang

Proc. Interspeech 2017 (2017) (to appear)

Incoherent idempotent ambisonics rendering

W. Bastiaan Kleijn, Andrew Allen , Jan Skoglund , Felicia Lim

2017 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (2017)

Joint Wideband Source Localization and Acquisition Based on a Grid-Shift Approach

Christos Tzagkarakis, Bastiaan Kleijn, Jan Skoglund

Keyword Spotting for Google Assistant Using Contextual Speech Recognition

Assaf Michaely , Carolina Parada , Frank Zhang, Gabor Simko , Petar Aleksic

ASRU 2017, IEEE

Language Modeling in the Era of Abundant Data

Ciprian Chelba

AI With the Best online conference. (2017)

Latent Sequence Decompositions

William Chan , Yu Zhang , Quoc Le , Navdeep Jaitly

ICLR (2017)

Multi-Accent Speech Recognition with Hierarchical Grapheme Based Models

Hasim Sak , Kanishka Rao

ICASSP 2017 (to appear)

Multichannel Signal Processing with Deep Neural Networks for Automatic Speech Recognition

Tara Sainath , Ron J. Weiss , Kevin Wilson , Bo Li , Arun Narayanan , Ehsan Variani , Michiel Bacchiani , Izhak Shafran , Andrew Senior , Kean Chin, Ananya Misra , Chanwoo Kim

IEEE /ACM Transactions on Audio, Speech, and Language Processing, vol. 25 (2017), pp. 965 - 979

On Lattice Generation for Large Vocabulary Speech Recognition

David Rybach , Johan Schalkwyk, Michael Riley

IEEE Automatic Speech Recognition and Understanding Workshop (ASRU), Okinawa, Japan (2017)

Optimizing expected word error rate via sampling for speech recognition

Matt Shannon

Parallel WaveNet: Fast High-Fidelity Speech Synthesis

Aäron van den Oord, Yazhe Li, Igor Babuschkin, Karen Simonyan, Oriol Vinyals , Koray Kavukcuoglu, George van den Driessche, Edward Lockhart, Luis Carlos Cobo Rus, Florian Stimberg, Norman Casagrande, Dominik Grewe, Seb Noury, Sander Dieleman, Erich Elsen , Nal Kalchbrenner, Heiga Zen , Alexander Graves, Helen King, Thomas Walters , Dan Belov, Demis Hassabis

NA, Google Deepmind, NA (2017)

Practically Efficient Nonlinear Acoustic Echo Cancellers Using Cascaded Block RLS and FLMS Adaptive Filters

Yiteng (Arden) Huang, Jan Skoglund , Alejandro Luebs

ICASSP (2017)

Raw Multichannel Processing Using Deep Neural Networks

Tara N. Sainath , Ron J. Weiss , Kevin W. Wilson , Arun Narayanan , Michiel Bacchiani , Bo Li , Ehsan Variani , Izhak Shafran , Andrew Senior , Kean Chin, Ananya Misra , Chanwoo Kim

New Era for Robust Speech Recognition: Exploiting Deep Learning, Springer (2017)

Robust Speech Recognition Based on Binaural Auditory Processing

Anjali Menon, Chanwoo Kim , Richard M. Stern

INTERSPEECH 2017 (2017), pp. 3872-3876

Robust and low-complexity blind source separation for meeting rooms

W. Bastiaan Kleijn, Felicia Lim

Proceedings Fifth Joint Workshop on Hands-free Speech Communication and Microphone Arrays (2017)

Sparse Non-negative Matrix Language Modeling: Maximum Entropy Flexibility on the Cheap

Ciprian Chelba , Diamantino Caseiro, Fadi Biadsy

The 18th Annual Conference of the International Speech Communication Association, Stockholm, Sweden, pp. 2725-2729 (to appear)

Speaker Diarization with LSTM

Quan Wang , Carlton Downey, Li Wan , Philip Andrew Mansfield, Ignacio Lopez Moreno

Streaming Small-Footprint Keyword Spotting Using Sequence-to-Sequence Models

Yanzhang (Ryan) He, Rohit Prabhavalkar , Kanishka Rao , Wei Li, Anton Bakhtin , Ian McGraw

Automatic Speech Recognition and Understanding (ASRU), 2017 IEEE Workshop on

Syllable-Based Acoustic Modeling with CTC-SMBR-LSTM

Zhongdi Qu, Parisa Haghani, Eugene Weinstein , Pedro Moreno

Tacotron: Towards End-to-End Speech Synthesis

Yuxuan Wang , RJ Skerry-Ryan , Daisy Stanton , Yonghui Wu , Ron J. Weiss , Navdeep Jaitly, Zongheng Yang, Ying Xiao , Zhifeng Chen , Samy Bengio , Quoc Le , Yannis Agiomyrgiannakis , Rob Clark , Rif A. Saurous

Trainable Frontend For Robust and Far-Field Keyword Spotting

Yuxuan Wang , Pascal Getreuer , Thad Hughes , Richard F. Lyon , Rif A. Saurous

Proc. IEEE ICASSP 2017, New Orleans, LA

Uncovering Latent Style Factors for Expressive Speech Synthesis

Yuxuan Wang , RJ Skerry-Ryan , Ying Xiao , Daisy Stanton , Joel Shor , Eric Battenberg , Rob Clark , Rif A. Saurous

NIPS Workshop on Machine Learning for Audio Signal Processing (ML4Audio) (2017) (to appear)

Uniform Multilingual Multi-Speaker Acoustic Model for Statistical Parametric Speech Synthesis of Low-Resourced Languages

Alexander Gutkin

Proc. of Interspeech 2017, ISCA, August 20–24, Stockholm, Sweden, pp. 2183-2187

Very Deep Convolutional Networks for End-to-End Speech Recognition

Yu Zhang , William Chan , Navdeep Jaitly

Wavenet based low rate speech coding

W. Bastiaan Kleijn, Felicia S. C. Lim , Alejandro Luebs , Jan Skoglund , Florian Stimberg, Quan Wang , Thomas C. Walters

arXiv preprint arXiv:1712.01120 (2017)

A subband-based stationary-component suppression method using harmanics and power ratio for reverberant speech recognition

Byung Joon Cho, Haeyong Kwon, Ji-Won Cho, Chanwoo Kim , Richard M. Stern, Hyung-Min Park

IEEE SIGNAL PROCESSING LETTERS, vol. 23 (2016), pp. 780-784

AN ACOUSTIC KEYSTROKE TRANSIENT CANCELER FOR SPEECH COMMUNICATION TERMINALS USING A SEMI-BLIND ADAPTIVE FILTER MODEL

Herbert Buchner, Simon Godsill, Jan Skoglund

ICASSP (2016)

AutoMOS: Learning a non-intrusive assessor of naturalness-of-speech

Brian Patton , Yannis Agiomyrgiannakis , Michael Terry, Kevin Wilson , Rif A. Saurous , D. Sculley

NIPS 2016 End-to-end Learning for Speech and Audio Processing Workshop (to appear)

Automatic Optimization of Data Perturbation Distributions for Multi-Style Training in Speech Recognition

Mortaza Doulaty, Richard Rose , Olivier Siohan

Proceedings of the IEEE 2016 Workshop on Spoken Language Technology (SLT2016)

BI-MAGNITUDE PROCESSING FRAMEWORK FOR NONLINEAR ACOUSTIC ECHO CANCELLATION ON ANDROID DEVICES

Yiteng (Arden) Huang , Jan Skoglund , Alejandro Luebs

International Workshop on Acoustic Signal Enhancement 2016 (IWAENC2016)

Building Statistical Parametric Multi-speaker Synthesis for Bangladeshi Bangla

Alexander Gutkin , Linne Ha, Martin Jansche , Oddur Kjartansson, Knot Pipatsrisawat, Richard Sproat

SLTU-2016 5th Workshop on Spoken Language Technologies for Under-resourced languages, 09-12 May 2016, Yogyakarta, Indonesia; Procedia Computer Science, Elsevier B.V., pp. 194-200

Complex Linear Projection (CLP): A Discriminative Approach to Joint Feature Extraction and Acoustic Modeling

Ehsan Variani , Tara N. Sainath , Izhak Shafran , Michiel Bacchiani

Interspeech 2016 (2016)

Contextual prediction models for speech recognition

Yoni Halpern, Keith Hall , Vlad Schogol, Michael Riley , Brian Roark , Gleb Skobeltsyn , Martin Baeuml

Proceedings of Interspeech 2016

Cross-lingual projection for class-based language models

Beat Gfeller, Vlad Schogol, Keith Hall

Directly Modeling Voiced and Unvoiced Components in Speech Waveforms by Neural Networks

Keiichi Tokuda, Heiga Zen

Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), IEEE (2016), pp. 5640-5644

Distilling Knowledge from Ensembles of Neural Networks for Speech Recognition

Austin Waters , Yevgen Chebotar

Interspeech (2016)

Distributed representation and estimation of WFST-based n-gram models

Cyril Allauzen , Michael Riley , Brian Roark

Proceedings of the ACL Workshop on Statistical NLP and Weighted Automata (StatFSM) (2016), pp. 32-41

End-to-End Text-Dependent Speaker Verification

Georg Heigold , Ignacio Moreno , Samy Bengio , Noam M. Shazeer

International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE (2016)

Factored Spatial and Spectral Multichannel Raw Waveform CLDNNs

Tara N. Sainath , Ron J. Weiss , Kevin W. Wilson , Arun Narayanan , Michiel Bacchiani

Fast, Compact, and High Quality LSTM-RNN Based Statistical Parametric Speech Synthesizers for Mobile Devices

Heiga Zen , Yannis Agiomyrgiannakis , Niels Egberts, Fergus Henderson , Przemysław Szczepaniak

Proc. Interspeech, San Francisco, CA, USA (2016)

Feature Learning with Raw-Waveform CLDNNs for Voice Activity Detection

Ruben Zazo, Tara N. Sainath , Gabor Simko , Carolina Parada

Flatstart-CTC: a new acoustic model training procedure for speech recognition

Andrew Senior , Hasim Sak , Kanishka Rao

ICASSP 2016

GLOBALLY OPTIMIZED LEAST-SQUARES POST-FILTERING FOR MICROPHONE ARRAY SPEECH ENHANCEMENT

Yiteng (Arden) Huang , Alejandro Luebs , Jan Skoglund , W. Bastiaan Kleijn

High quality agreement-based semi-supervised training data for acoustic modeling

Félix de Chaumont Quitry , Asa Oines, Pedro Moreno , Eugene Weinstein

2016 IEEE Workshop on Spoken Language Technology

Learning Compact Recurrent Neural Networks

Zhiyun Lu, Vikas Sindhwani , Tara Sainath

IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2016

Learning N-gram Language Models from Uncertain Data

Vitaly Kuznetsov , Hank Liao , Mehryar Mohri , Michael Riley , Brian Roark

Learning Personalized Pronunciations for Contact Names Recognition

Tony Bruguier , Fuchun Peng , Francoise Beaufays

Interspeech 2016 (to appear)

Listen, Attend and Spell: A Neural Network for Large Vocabulary Conversational Speech Recognition

William Chan , Navdeep Jaitly, Quoc V. Le , Oriol Vinyals

Lower Frame Rate Neural Network Acoustic Models

Modeling Time-Frequency Patterns with LSTM vs. Convolutional Architectures for LVCSR Tasks

Tara N. Sainath , Bo Li

Proc. Interspeech, ISCA (2016) (to appear)

Multi-Language Multi-Speaker Acoustic Modeling for LSTM-RNN based Statistical Parametric Speech Synthesis

Bo Li , Heiga Zen

Neural Network Adaptive Beamforming for Robust Multichannel Speech Recognition

Bo Li , Tara N. Sainath , Ron J. Weiss , Kevin W. Wilson , Michiel Bacchiani

Proc. Interspeech, ISCA (2016)

Neural Speech Recognizer: Acoustic-to-Word LSTM Model for Large Vocabulary Speech Recognition

Hagen Soltau, Hank Liao , Hasim Sak

ArXiv e-prints (2016)

ON PRE-FILTERING STRATEGIES FOR THE GCC-PHAT ALGORITHM

Hong-Goo Kang, Michael Graczyk, Jan Skoglund

International Workshop on Acoustic Signal Enhancement 2016 (IWAENC 2016)

On The Compression Of Recurrent Neural Networks With An Application To LVCSR Acoustic Modeling For Embedded Speech Recognition

Rohit Prabhavalkar , Ouais Alsharif , Antoine Bruguier , Ian McGraw

Proceedings of International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE (2016)

On the Efficient Representation and Execution of Deep Acoustic Models

Raziel Alvarez , Rohit Prabhavalkar , Anton Bakhtin

Proceedings of Annual Conference of the International Speech Communication Association (Interspeech) (2016)

Personalized Speech Recognition On Mobile Devices

Ian McGraw, Rohit Prabhavalkar , Raziel Alvarez , Montse Gonzalez Arenas, Kanishka Rao , David Rybach , Ouais Alsharif , Hasim Sak , Alexander Gruenstein , Françoise Beaufays , Carolina Parada

Power-Normalized Cepstral Coefficients (PNCC) for Robust Speech Recognition

Chanwoo Kim , Richard M. Stern

IEEE/ACM TRANSACTIONS ON AUDIO, SPEECH, AND LANGUAGE PROCESSING,, vol. 24 (2016), pp. 1315-1329

Predicting Pronunciations with Syllabification and Stress with Recurrent Neural Networks

Daan van Esch, Kanishka Rao , Mason Chua

Proceedings of InterSpeech 2016 (to appear)

Pynini: A Python library for weighted finite-state grammar compilation

Kyle Gorman

Proceedings of the ACL Workshop on Statistical NLP and Weighted Automata (2016), pp. 75-80

Recent Advances in Google Real-time HMM-driven Unit Selection Synthesizer

Xavi Gonzalvo , Siamak Tazari, Chun-an Chan, Markus Becker, Alexander Gutkin , Hanna Silen

INTERSPEECH 2016, Sep 8-12, San Francisco, USA, pp. 2238-2242

Reducing the Computational Complexity of Multimicrophone Acoustic Models with Integrated Feature Extraction

Tara N. Sainath , Arun Narayanan , Ron J. Weiss , Ehsan Variani , Kevin W. Wilson , Michiel Bacchiani , Izhak Shafran

Robust Estimation of Reverberation Time Using Polynomial Roots

Ian Kelly , Francis Boland, Jan Skoglund

AES 60th Conference on Dereverberation and Reverberation of Audio, Music, and Speech, Google Ireland Ltd. (2016)

Selection and Combination of Hypotheses for Dialectal Speech Recognition

Victor Soto, Olivier Siohan , Mohamed Elfeky , Pedro J. Moreno

Semantic Model for Fast Tagging of Word Lattices

Leonid Velikovich

IEEE Spoken Language Technology (SLT) Workshop (2016) (to appear)

THE MATCHING-MINIMIZATION ALGORITHM, THE INCA ALGORITHM AND A MATHEMATICAL FRAMEWORK FOR VOICE CONVERSION WITH UNALIGNED CORPORA.

Yannis Agiomyrgiannakis

ICASSP, IEEE (2016)

TTS for Low Resource Languages: A Bangla Synthesizer

Alexander Gutkin , Linne Ha, Martin Jansche , Knot Pipatsrisawat, Richard Sproat

10th edition of the Language Resources and Evaluation Conference, 23-28 May 2016, European Language Resources Association (ELRA), Portorož, Slovenia, pp. 2005-2010

Towards Acoustic Model Unification Across Dialects

Austin Waters , Meysam Bastani, Mohamed G. Elfeky , Pedro Moreno , Xavier Velez

Unsupervised Context Learning For Speech Recognition

Assaf Michaely , Justin Scheiner, Mohammadreza Ghodsi , Petar Aleksic , Zelin Wu

Spoken Language Technology (SLT) Workshop, IEEE (2016)

Unsupervised Word Segmentation and Lexicon Discovery Using Acoustic Word Embeddings

Aren Jansen , Herman Kamper, Sharon Goldwater

IEEE Transactions on Audio, Speech, and Language Processing (2016)

Using instantaneous frequency and aperiodicity detection to estimate FO for high-quality speech synthesis

Hideki Kawahara, Yannis Agiomyrgiannakis , Heiga Zen

Proc. ISCA SSW9 (2016), pp. 238-245

VOICE MORPHING THAT IMPROVES TTS QUALITY USING AN OPTIMAL DYNAMIC FREQUENCY WARPING-AND-WEIGHTING TRANSFORM

Yannis Agiomyrgiannakis , Zoe Roupakia

A 6 µW per Channel Analog Biomimetic Cochlear Implant Processor Filterbank Architecture With Across Channels AGC

Guang Wang, Richard F. Lyon , Emmanuel M. Drakakis

IEEE Transactions on Biomedical Circuits and Systems, vol. 9 (2015), pp. 72-86

A Gaussian Mixture Model Layer Jointly Optimized with Discriminative Features within A Deep Neural Network Architecture

Ehsan Variani , Erik McDermott , Georg Heigold

ICASSP, IEEE (2015)

Acoustic Modeling for Speech Synthesis: from HMM to RNN

IEEE ASRU, Scottsdale, Arizona, U.S.A. (2015)

Acoustic Modeling in Statistical Parametric Speech Synthesis - From HMM to LSTM-RNN

Proc. MLSLP (2015)

Acoustic Modelling with CD-CTC-SMBR LSTM RNNS

Andrew Senior , Hasim Sak , Felix de Chaumont Quitry , Tara N. Sainath , Kanishka Rao

ASRU (2015)

Automatic Gain Control and Multi-style Training for Robust Small-Footprint Keyword Spotting with Deep Neural Networks

Rohit Prabhavalkar , Raziel Alvarez , Carolina Parada , Preetum Nakkiran, Tara Sainath

Proceedings of International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE (2015), pp. 4704-4708

Automatic Pronunciation Verification for Speech Recognition

Kanishka Rao , Fuchun Peng , Françoise Beaufays

ICASSP (2015)

Bringing Contextual Information to Google Speech Recognition

Petar Aleksic , Mohammadreza Ghodsi , Assaf Michaely , Cyril Allauzen , Keith Hall , Brian Roark , David Rybach , Pedro Moreno

Interspeech 2015, International Speech Communications Association

Composition-based on-the-fly rescoring for salient n-gram biasing

Keith Hall , Eunjoon Cho, Cyril Allauzen , Francoise Beaufays , Noah Coccaro, Kaisuke Nakajima, Michael Riley , Brian Roark , David Rybach , Linda Zhang

Compressing Deep Neural Networks using a Rank-Constrained Topology

Preetum Nakkiran, Raziel Alvarez , Rohit Prabhavalkar , Carolina Parada

Proceedings of Annual Conference of the International Speech Communication Association (Interspeech), ISCA (2015), pp. 1473-1477

Context dependent phone models for LSTM RNN acoustic modelling

Andrew W. Senior , Hasim Sak , Izhak Shafran

ICASSP (2015), pp. 4585-4589

Convolutional Neural Networks for Small-Footprint Keyword Spotting

Tara Sainath , Carolina Parada

Interspeech (2015)

Convolutional, Long Short-Term Memory, Fully Connected Deep Neural Networks

Tara Sainath , Oriol Vinyals , Andrew Senior , Hasim Sak

DETECTION AND SUPPRESSION OF KEYBOARD TRANSIENT NOISE IN AUDIO STREAMS WITH AUXILIARY KEYBED MICROPHONE

Simon Godsill, Herbert Buchner, Jan Skoglund

ICASSP 2015, IEEE

DIRECT-TO-REVERBERANT RATIO ESTIMATION USING A NULL-STEERED BEAMFORMER

James Eaton, Alastair Moore, Patrick Naylor, Jan Skoglund

Deep Learning for Acoustic Modeling in Parametric Speech Generation: A systematic review of existing techniques and future trends

Zhen-Hua Ling, Shiyin Kang, Heiga Zen , Andrew Senior , Mike Schuster , Xiao-Jun Qian, Helen Meng, Li Deng

IEEE Signal Processing Magazine, vol. 32 (2015), pp. 35-52

Directly Modeling Speech Waveforms by Neural Networks for Statistical Parametric Speech Synthesis

Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), IEEE (2015), pp. 4215-4219

Fast and Accurate Recurrent Neural Network Acoustic Models for Speech Recognition

Hasim Sak , Andrew W. Senior , Kanishka Rao , Françoise Beaufays

CoRR, vol. abs/1507.06947 (2015)

Fix It Where It Fails: Pronunciation Learning by Mining Error Corrections from Speech Logs

Zhenzhen Kou, Daisy Stanton , Fuchun Peng , Françoise Beaufays , Trevor Strohman

Garbage Modeling for On-device Speech Recognition

Christophe Van Gysel, Leonid Velikovich , Ian McGraw, Françoise Beaufays

Interspeech 2015, International Speech Communications Association (to appear)

Geo-location for Voice Search Language Modeling

Ciprian Chelba , Xuedong Zhang, Keith Hall

Interspeech 2015, International Speech Communications Association, pp. 1438-1442

Grapheme-to-Phoneme Conversion Using Long Short-Term Memory Recurrent Neural Networks

Kanishka Rao , Fuchun Peng , Hasim Sak , Françoise Beaufays

Improved recognition of contact names in voice commands

Petar Aleksic , Cyril Allauzen , David Elson, Aleks Kracun, Diego Melendo Casado, Pedro J. Moreno

ICASSP 2015

Stanford Information Theory Forum (2015)

Large Vocabulary Automatic Speech Recognition for Children

Hank Liao , Golan Pundak , Olivier Siohan , Melissa Carroll, Noah Coccaro, Qi-Ming Jiang, Tara N. Sainath , Andrew Senior , Françoise Beaufays , Michiel Bacchiani

Large-scale, sequence-discriminative, joint adaptive training for masking-based robust ASR

Arun Narayanan , Ananya Misra , Kean Chin

INTERSPEECH-2015, ISCA, pp. 3571-3575

Learning acoustic frame labeling for speech recognition with recurrent neural networks

Hasim Sak , Andrew W. Senior , Kanishka Rao , Ozan Irsoy, Alex Graves, Françoise Beaufays, Johan Schalkwyk

ICASSP (2015), pp. 4280-4284

Learning the Speech Front-end with Raw Waveform CLDNNs

Tara Sainath , Ron J. Weiss , Kevin Wilson , Andrew W. Senior , Oriol Vinyals

Listen, Attend and Spell

CoRR, vol. abs/1508.01211 (2015)

Locally-Connected and Convolutional Neural Networks for Small Footprint Speaker Recognition

Yu-hsin Chen, Ignacio Lopez Moreno , Tara Sainath , Mirkó Visontai, Raziel Alvarez , Carolina Parada

Long Short-Term Memory Language Models with Additive Morphological Features for Automatic Speech Recognition

Daniel Renshaw, Keith B. Hall

IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP) (2015)

Multi-Dialectical Languages Effect on Speech Recognition

Mohamed Elfeky , Pedro J. Moreno , Victor Soto

International Conference on Natural Language and Speech Processing (2015)

Multitask learning and system combination for automatic speech recognition

Olivier Siohan , David Rybach

2015 IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU)

Pruning Sparse Non-negative Matrix N-gram Language Models

Joris Pelemans, Noam M. Shazeer, Ciprian Chelba

Proceedings of Interspeech 2015, ISCA, pp. 1433-1437

Query-by-Example Keyword Spotting Using Long Short-Term Memory Networks

Guoguo Chen, Carolina Parada , Tara N. Sainath

Rapid Vocabulary Addition to Context-Dependent Decoder Graphs

Cyril Allauzen , Michael Riley

Interspeech 2015

Sequence-based Class Tagging for Robust Transcription in ASR

Lucy Vasserman , Vlad Schogol, Keith Hall

Sound source separation algorithm using phase difference and angle distribution modeling near the target

Chanwoo Kim , Kean Chin

INTERSPEECH 2015, pp. 751-755

Sparse Non-negative Matrix Language Modeling for Geo-annotated Query Session Data

Ciprian Chelba , Noam M. Shazeer

Automatic Speech Recognition and Understanding Workshop (ASRU 2015) Proceedings, IEEE, to appear (to appear)

Speaker Location and Microphone Spacing Invariant Acoustic Modeling from Raw Multichannel Waveforms

Tara N. Sainath , Ron J. Weiss , Kevin Wilson , Arun Narayanan , Michiel Bacchiani , Andrew Senior

Speech Acoustic Modeling from Raw Multichannel Waveforms

Yedid Hoshen, Ron Weiss , Kevin W Wilson

International Conference on Acoustics, Speech, and Signal Processing, IEEE (2015)

Statistical parametric speech synthesis: from HMM to LSTM-RNN

RTTH Summer School on Speech Technology -- A Deep Learning Perspective, Barcelona, Spain (2015)

Telluride Decoding Toolbox

Sahar Akram, Alain de Cheveigné, Peter Udo Diehl, Emily Graber, Carina Graversen, Jens Hjortkjaer, Nima Mesgarani, Lucas Parra, Ulrich Pomper, Shihab Shamma, Jonathan Simon, Malcolm Slaney , Daniel Wong

Institute for Neuroinformatics (2015)

Unidirectional Long Short-Term Memory Recurrent Neural Network with Recurrent Output Layer for Low-Latency Speech Synthesis

Heiga Zen , Hasim Sak

Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), IEEE (2015), pp. 4470-4474

ViSQOL: an objective speech quality model

Andrew Hines, Jan Skoglund , Anil Kokaram , Naomi Harte

EURASIP Journal on Audio, Speech, and Music Processing, vol. 2015 (13) (2015), pp. 1-18

Vocaine the Vocoder and Applications in Speech Synthesis

ICASSP, IEEE (2015) (to appear)

A big data approach to acoustic model training corpus selection

Olga Kapralova , John Alex, Eugene Weinstein , Pedro Moreno , Olivier Siohan

Conference of the International Speech Communication Association (Interspeech) (2014)

An Analysis of the Effect of Larynx-Synchronous Averaging on Dereverberation of Voiced Speech

Alastair H Moore, Patrick A Naylor, Jan Skoglund

Proceedings of European Signal Processing Conference (EUSIPCO) 2014

Asynchronous Stochastic Optimization for Sequence Training of Deep Neural Networks

Georg Heigold , Erik McDermott , Vincent Vanhoucke , Andrew Senior , Michiel Bacchiani

Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), IEEE, Firenze, Italy (2014)

Asynchronous Stochastic Optimization for Sequence Training of Deep Neural Networks: Towards Big Data

Erik McDermott , Georg Heigold , Pedro Moreno , Andrew Senior, Michiel Bacchiani

Interspeeech, ISCA (2014)

Asynchronous, Online, GMM-free Training of a Context Dependent Acoustic Model for Speech Recognition

M. Bacchiani , A. Senior , G. Heigold

Proceedings of the European Conference on Speech Communication and Technology (2014) (to appear)

Automatic Language Identification Using Deep Neural Networks

Ignacio Lopez-Moreno , Javier Gonzalez-Dominguez, Oldrich Plchot

Proc. ICASSP, IEEE (2014)

Automatic Language Identification using Long Short-Term Memory Recurrent Neural Networks

Javier Gonzalez-Dominguez, Ignacio Lopez-Moreno , Hasim Sak

Interspeech (2014)

Autoregressive Product of Multi-frame Predictions Can Improve the Accuracy of Hybrid Models

Navdeep Jaitly, Vincent Vanhoucke , Geoffrey Hinton

Proceedings of Interspeech 2014

Backoff Inspired Features for Maximum Entropy Language Models

Fadi Biadsy , Keith Hall , Pedro Moreno , Brian Roark

Proceedings of Interspeech, ISCA (2014)

Computer-aided quality assurance of an Icelandic pronunciation dictionary

Martin Jansche

LREC 2014, Reykjavik

Context Dependent State Tying for Speech Recognition using Deep Neural Network Acoustic Models

M. Bacchiani , D. Rybach

Proceedings of the International Conference on Acoustics,Speech and Signal Processing (2014)

Deep Mixture Density Networks for Acoustic Modeling in Statistical Parametric Speech Synthesis

Heiga Zen , Andrew Senior

Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), IEEE (2014), pp. 3872-3876

Deep Neural Networks for Small Footprint Text-dependent Speaker Verification

Ehsan Variani , Xin Lei, Erik McDermott , Ignacio Lopez Moreno , Javier Gonzalez-Dominguez

Direct construction of compact context-dependency transducers from data

David Rybach , Michael Riley , Chris Alberti

Computer Speech & Language, vol. 28 (2014), pp. 177-191

Discriminative pronunciation modeling for dialectal speech recognition

Maider Lehr, Kyle Gorman , Izhak Shafran

Proc. Interspeech (2014) (to appear)

Encoding Linear Models As Weighted Finite-State Transducers

Ke Wu, Cyril Allauzen , Keith Hall , Michael Riley , Brian Roark

Interspeech 2014, ISCA, pp. 1258-1262

Fine Context, Low-rank, Softplus Deep Neural Networks for Mobile Speech Recognition

Andrew Senior , Xin Lei

Proc. ICASSP (2014) (to appear)

Frame by Frame Language Identification in Short Utterances using Deep Neural Networks

Javier Gonzalez-Dominguez, Ignacio Lopez-Moreno , Pedro J. Moreno , Joaquin Gonzalez-Rodriguez

Neural Networks Special Issue: Neural Network Learning in Big Data (2014)

GMM-Free DNN Training

A. Senior , G. Heigold , M. Bacchiani , H. Liao

Improving DNN Speaker Independence with I-vector Inputs

Andrew Senior , Ignacio Lopez-Moreno

JustSpeak: Enabling Universal Voice Control on Android

Yu Zhong , T. V. Raman , Casey Burkhardt , Fadi Biadsy , Jeffrey P. Bigham

Large-Scale Speaker Identification

Ludwig Schmidt, Matthew Sharifi, Ignacio Lopez-Moreno

Long Short-Term Memory Based Recurrent Neural Network Architectures for Large Vocabulary Speech Recognition

Hasim Sak , Andrew W. Senior , Françoise Beaufays

CoRR, vol. abs/1402.1128 (2014)

Long short-term memory recurrent neural network architectures for large scale acoustic modeling

INTERSPEECH (2014), pp. 338-342

Pronunciation Learning for Named-Entities through Crowd-Sourcing

Attapol Rutherford, Fuchun Peng , Françoise Beaufays

Proceedings of Interspeech (2014)

Robust speech recognition in reverberant environments using subband-based steady-state monaural and binaural suppression

Hyung-Min Park, Matthew Maciejewski, Chanwoo Kim , Richard M. Stern

INTERSPEECH (2014), pp. 2715-2718

Robust speech recognition using temporal masking and thresholding algorithm

Chanwoo Kim , Kean Chin, Michiel Bacchiani , R. M. Stern

INTERSPEECH-2014, pp. 2734-2738

Sequence Discriminative Distributed Training of Long Short-Term Memory Recurrent Neural Networks

Hasim Sak , Oriol Vinyals , Georg Heigold , Andrew Senior, Erik McDermott , Rajat Monga , Mark Mao

Sinusoidal Interpolation Across Missing Data

W. Bastiaan Kleijn, Turaj Zakizadeh Shabestary, Jan Skoglund

International Workshop on Acoustic Signal Enhancement 2014 (IWAENC 2014), pp. 71-75

Small-Footprint Keyword Spotting using Deep Neural Networks

Guoguo Chen, Carolina Parada , Georg Heigold

ICASSP, IEEE (2014)

Statistical Parametric Speech Synthesis

UKSpeech Conference, Edinburgh, UK (2014)

Text-To-Speech with cross-lingual Neural Network-based grapheme-to-phoneme models

Xavi Gonzalvo , Monika Podsiadlo

Training Data Selection Based On Context-Dependent State Matching

Olivier Siohan

Proceedings of ICASSP 2014

Word Embeddings for Speech Recognition

Samy Bengio , Georg Heigold

Proceedings of the 15th Conference of the International Speech Communication Association, Interspeech (2014)

A FREQUENCY-WEIGHTED POST-FILTERING TRANSFORM FOR COMPENSATION OF THE OVER-SMOOTHING EFFECT IN HMM-BASED SPEECH SYNTHESIS

Yannis Agiomyrgiannakis , Florian Eyben

ICASSP, IEEE (2013)

Accurate and Compact Large Vocabulary Speech Recognition on Mobile Devices

Xin Lei, Andrew Senior , Alexander Gruenstein , Jeffrey Sorensen

Interspeech (2013)

An Empirical study of learning rates in deep neural networks for speech recognition

Andrew Senior , Georg Heigold , Marc'aurelio Ranzato, Ke Yang

Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), IEEE, Vancouver, CA (2013) (to appear)

Deep Learning in Speech Synthesis

8th ISCA Speech Synthesis Workshop, Barcelona, Spain (2013)

Deep Neural Networks with Auxiliary Gaussian Mixture Models for Real-Time Speech Recognition

Xin Lei, Hui Lin , Georg Heigold

Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), IEEE, Vancouver, CA (2013)

Empirical Exploration of Language Modeling for the google.com Query Stream as Applied to Mobile Voice Search

Ciprian Chelba , Johan Schalkwyk

Mobile Speech and Advanced Natural Language Solutions, Springer Science+Business Media, New York (2013), pp. 197-229

Language Model Verbalization for Automatic Speech Recognition

Hasim Sak , Françoise Beaufays , Kaisuke Nakajima, Cyril Allauzen

Proc ICASSP, IEEE (2013)

Language Modeling Capitalization

Françoise Beaufays , Brian Strope

Proc ICASSP, IEEE (2013) (to appear)

Large Scale Distributed Acoustic Modeling With Back-off N-grams

Ciprian Chelba , Peng Xu , Fernando Pereira , Thomas Richardson

IEEE Transactions on Audio, Speech and Language Processing, vol. 21 (2013), pp. 1158-1169

ICSI, Berkeley, California (2013)

Large scale deep neural network acoustic modeling with semi-supervised training data for YouTube video transcription

Hank Liao , Erik McDermott , Andrew Senior

ASRU (2013)

Mixture of mixture n-gram language models

Hasim Sak , Cyril Allauzen , Kaisuke Nakajima, Françoise Beaufays

ASRU (2013), pp. 31-36

Monitoring the Effects of Temporal Clipping on VoIP Speech Quality

Interspeech 2013, pp. 1188-1192

Multiframe Deep Neural Networks for Acoustic Modeling

Vincent Vanhoucke , Matthieu Devin , Georg Heigold

Multilingual acoustic models using distributed deep neural networks

Georg Heigold , Vincent Vanhoucke , Andrew Senior , Patrick Nguyen, Marc'aurelio Ranzato, Matthieu Devin , Jeff Dean

On Rectified Linear Units For Speech Processing

M.D. Zeiler, M. Ranzato, R. Monga , M. Mao, K. Yang , Q.V. Le , P. Nguyen, A. Senior , V. Vanhoucke , J. Dean , G.E. Hinton

38th International Conference on Acoustics, Speech and Signal Processing (ICASSP), Vancouver (2013)

Pre-Initialized Composition for Large-Vocabulary Speech Recognition

Interspeech 2013, 666 – 670

RAPID ADAPTATION FOR MOBILE SPEECH APPLICATIONS

M. Bacchiani

Proceedings of the International Conference on Acoustics,Speech and Signal Processing (2013)

Rate-Distortion Optimization for Multichannel Audio Compression

Minyue Li, Jan Skoglund , W. Bastiaan Kleijn

2013 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA)

Recurrent Neural Networks for Voice Activity Detection

Thad Hughes , Keir Mierle

ICASSP, IEEE (2013), pp. 7378-7382

Robustness of Speech Quality Metrics to Background Noise and Network Degradations: Comparing VISQOL, PESQ and POLQA

IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), IEEE (2013), pp. 3697-3701

Search Results Based N-Best Hypothesis Rescoring With Maximum Entropy Classification

Fuchun Peng , Scott Roy, Ben Shahshahani, Françoise Beaufays

Proceedings of ASRU (2013)

Smoothed marginal distribution constraints for language modeling

Brian Roark , Cyril Allauzen , Michael Riley

Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (ACL) (2013), pp. 43-52

Speaker Adaptation of Context Dependent Deep Neural Networks

International Conference of Acoustics, Speech, and Signal Processing. (2013)

Speech and Natural Language: Where Are We Now And Where Are We Headed?

Mobile Voice Conference, San Francisco (2013)

Statistical Parametric Speech Synthesis Using Deep Neural Networks

Heiga Zen , Andrew Senior , Mike Schuster

Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), IEEE (2013), pp. 7962-7966

Written-Domain Language Modeling for Automatic Speech Recognition

Hasim Sak , Yun-hsuan Sung , Françoise Beaufays , Cyril Allauzen

iVector-based Acoustic Data Selection

Olivier Siohan , Michiel Bacchiani

Proceedings of Interspeech (2013)

Application Of Pretrained Deep Neural Networks To Large Vocabulary Speech Recognition

Navdeep Jaitly, Patrick Nguyen, Andrew Senior , Vincent Vanhoucke

Proceedings of Interspeech 2012

Buildling adaptive dialogue systems via Bayes-adaptive POMDP

Shaowei Png , Joelle Pineau, B. Chaib-draa

IEEE Journal of Selected Topics in Signal Processing, vol. vol.6(8). 2012. (2012), pp. 917-927

Chapter 17: Uncertainty Decoding, In Virtanen, Singh, & Raj (Eds.) Techniques for Noise Robustness in Automatic Speech Recognition.

Wiley (2012), pp. 463-485

Continuous Space Discriminative Language Modeling

Puyang Xu, Sanjeev Khudanpur, Maider Lehr, Emily Prud’hommeaux, Nathan Glenn, Damianos Karakos, Brian Roark , Kenji Sagae, Murat Saraclar, Izhak Shafran , Dan Bikel, Chris Callison-Burch, Yuan Cao, Keith Hall , Eva Hasler, Philipp Koehn, Adam Lopez, Matt Post, Darcey Riley

ICASSP 2012

Deep Neural Networks for Acoustic Modeling in Speech Recognition

Geoffrey Hinton , Li Deng, Dong Yu, George Dahl, Abdel-rahman Mohamed, Navdeep Jaitly, Andrew Senior , Vincent Vanhoucke , Patrick Nguyen, Tara Sainath , Brian Kingsbury

Signal Processing Magazine (2012)

Distributed Acoustic Modeling with Back-off N-grams

Proceedings of ICASSP 2012, IEEE, pp. 4129-4132

Distributed Discriminative Language Models for Google Voice Search

Preethi Jyothi, Leif Johnson , Ciprian Chelba , Brian Strope

Proceedings of ICASSP 2012, IEEE, pp. 5017-5021

Estimating Word-Stability During Incremental Speech Recognition

Ian McGraw, Alexander Gruenstein

Interspeech (2012)

Exemplar-Based Processing for Speech Recognition: An Overview

Tara N. Sainath , Bhuvana Ramabhadran, David Nahamoo, Dimitri Kanevsky, Dirk Van Compernolle, Kris Demuynck, Jort F. Gemmeke , Jerome R. Bellegarda, Shiva Sundaram

IEEE Signal Process. Mag., vol. 29 (2012), pp. 98-113

Google's Cross-Dialect Arabic Voice Search

Fadi Biadsy , Pedro J. Moreno , Martin Jansche

IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2012), pp. 4441-4444

Hallucinated N-Best Lists for Discriminative Language Modeling

Kenji Sagae, Maider Lehr, Emily Tucker Prud’hommeaux, Puyang Xu, Nathan Glenn, Damianos Karakos, Sanjeev Khudanpur, Brian Roark , Murat Saraçlar, Izhak Shafran , Daniel M. Bikel, Chris Callison-Burch, Yuan Cao, Keith Hall , Eva Hassler, Philipp Koehn, Adam Lopez, Matt Post, Darcey Riley

Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP) (2012)

Haptic Voice Recognition Grand Challenge

K. Sim, S. Zhao, K. Yu, H. Liao

14th ACM International Conference on Multimodal Interaction. (2012)

IMPROVED PREDICTION OF NEARLY-PERIODIC SIGNALS

Bastiaan Kleijn, Jan Skoglund

International Workshop on Acoustic Signal Enhancement 2012 (IWAENC2012)

Investigations on Exemplar-Based Features for Speech Recognition Towards Thousands of Hours of Unsupervised, Noisy Data

Georg Heigold , Patrick Nguyen, Mitchel Weintraub, Vincent Vanhoucke

Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), IEEE, Kyoto, Japan (2012), pp. 4437-4440

Japanese and Korean Voice Search

Mike Schuster , Kaisuke Nakajima

International Conference on Acoustics, Speech and Signal Processing, IEEE (2012), pp. 5149-5152

Language Modeling for Automatic Speech Recognition Meets the Web: Google Search by Voice

Ciprian Chelba , Johan Schalkwyk, Boulos Harb , Carolina Parada , Cyril Allauzen , Leif Johnson , Michael Riley , Peng Xu , Preethi Jyothi, Thorsten Brants, Vida Ha, Will Neveitt

University of Toronto (2012)

Large Scale Language Modeling in Automatic Speech Recognition

Ciprian Chelba , Dan Bikel, Maria Shugrina, Patrick Nguyen, Shankar Kumar

Google (2012)

Large-scale Discriminative Language Model Reranking for Voice Search

Proceedings of the NAACL-HLT 2012 Workshop: Will We Ever Really Replace the N-gram Model? On the Future of Language Modeling for HLT, Association for Computational Linguistics, pp. 41-49

Learning improved linear transforms for speech recognition

Andrew Senior , Youngmin Cho, Jason Weston

ICASSP, IEEE (2012)

Music Models for Music-Speech Separation

Thad Hughes , Trausti Kristjansson

ICASSP, IEEE (2012), pp. 4917-4920

Optimal Size, Freshness and Time-frame for Voice Search Vocabulary

Maryam Kamvar , Ciprian Chelba

Recognition of Multilingual Speech in Mobile Applications

Hui Lin , Jui-Ting Huang, Francoise Beaufays , Brian Strope, Yun-hsuan Sung

ICASSP (2012)

Recurrent Neural Networks for Noise Reduction in Robust ASR

Andrew Maas, Quoc V. Le , Tyler M. O’Neil, Oriol Vinyals , Patrick Nguyen, Andrew Y. Ng

INTERSPEECH (2012)

Semi-supervised Discriminative Language Modeling for Turkish ASR

Murat Saraçlar, Daniel M. Bikel, Keith Hall , Kenji Sagae

2012 IEEE International Conference on Acoustics, Speech, and Signal Processing Proceedings, IEEE, Kyoto, Japan

Spectral Intersections for Non-Stationary Signal Separation

Trausti Kristjansson, Thad Hughes

Proceedings of InterSpeech 2012, Portland, OR

Speech/Nonspeech Segmentation in Web Videos

Ananya Misra

Proceedings of InterSpeech 2012

VISQOL: THE VIRTUAL SPEECH QUALITY OBJECTIVE LISTENER

Voice Query Refinement

Cyril Allauzen , Edward Benson, Ciprian Chelba , Michael Riley , Johan Schalkwyk

A Web-Based Tool for Developing Multilingual Pronunciation Lexicons

Samantha Ainsley , Linne Ha, Martin Jansche , Ara Kim, Masayuki Nanzawa

12th Annual Conference of the International Speech Communication Association (Interspeech 2011), pp. 3331-3332

Bayesian Language Model Interpolation for Mobile Speech Input

Interspeech 2011, pp. 1429-1432

Deploying Google Search by Voice in Cantonese

Yun-hsuan Sung , Martin Jansche , Pedro Moreno

12th Annual Conference of the International Speech Communication Association (Interspeech 2011), pp. 2865-2868

Discriminative Features for Language Identification

C. Alberti, M. Bacchiani

INTERSPEECH (2011)

Improving the speed of neural networks on CPUs

Vincent Vanhoucke , Andrew Senior , Mark Z. Mao

Deep Learning and Unsupervised Feature Learning Workshop, NIPS 2011

Ciprian Chelba , Johan Schalkwyk, Boulos Harb , Carolina Parada , Cyril Allauzen , Michael Riley , Peng Xu , Thorsten Brants, Vida Ha, Will Neveitt

OGI/OHSU Seminar Series, Portland, Oregon, USA (2011)

Recognizing English Queries in Mandarin Voice Search

Hung-An Chang, Yun-hsuan Sung , Brian Strope, Francoise Beaufays

ICASSP (2011)

Speech Retrieval

Ciprian Chelba , Timothy J. Hazen, Bhuvana Ramabhadran, Murat Saraçlar

Spoken Language Understanding, John Wiley and Sons, Ltd (2011), pp. 417-446

Summary of Opus listening test results

Christian Hoene, Jean-Marc Valin, Koen Vos, Jan Skoglund

IETF, IETF (2011)

TechWare: Mobile Media Search Resources [Best of the Web]

Z. Liu, M. Bacchiani

IEEE Signal Processing Magazine, vol. 28 (2011), pp. 142-145

Unsupervised Testing Strategies for ASR

Brian Strope, Doug Beeferman, Alexander Gruenstein , Xin Lei

Interspeech 2011, pp. 1685-1688

Challenges in Automatic Speech Recognition

Ciprian Chelba , Johan Schalkwyk, Michiel Bacchiani

Interspeech 2010

Decision Tree State Clustering with Word and Syllable Features

Hank Liao , Chris Alberti , Michiel Bacchiani , Olivier Siohan

Interspeech, ISCA (2010), 2958 – 2961

Discriminative Topic Segmentation of Text and Speech

Mehryar Mohri , Pedro Moreno , Eugene Weinstein

International Conference on Artificial Intelligence and Statistics (AISTATS) (2010)

Google Search by Voice: A Case Study

Johan Schalkwyk, Doug Beeferman, Francoise Beaufays , Bill Byrne , Ciprian Chelba , Mike Cohen, Maryam Garrett , Brian Strope

Advances in Speech Recognition: Mobile Environments, Call Centers and Clinics, Springer (2010), pp. 61-90

On-Demand Language Model Interpolation for Mobile Speech Input

Brandon Ballinger, Cyril Allauzen , Alexander Gruenstein , Johan Schalkwyk

Interspeech (2010), pp. 1812-1815

Search by Voice in Mandarin Chinese

Jiulong Shan, Genqing Wu, Zhihong Hu, Xiliu Tang, Martin Jansche , Pedro J. Moreno

Interspeech 2010, pp. 354-357

Unsupervised Discovery and Training of Maximally Dissimilar Cluster Models

Francoise Beaufays , Vincent Vanhoucke , Brian Strope

Proc Interspeech (2010)

A new quality measure for topic segmentation of text and speech

Mehryar Mohri , Pedro J. Moreno , Eugene Weinstein

Conference of the International Speech Communication Association (Interspeech) (2009)

Restoring Punctuation and Capitalization in Transcribed Speech

Agustín Gravano, Martin Jansche , Michiel Bacchiani

IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP) (2009), pp. 4741-4744

Revisiting Graphemes with Increasing Amounts of Data

Yun-Hsuan Sung , Thad Hughes , Francoise Beaufays , Brian Strope

ICASSP, IEEE (2009)

Web-derived Pronunciations

Arnab Ghoshal, Martin Jansche , Sanjeev Khudanpur, Michael Riley , Morgan Ulinski

IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP) (2009), pp. 4289-4292

Confidence Scores for Acoustic Model Adaptation

C. Gollan, M. Bacchiani

Proceedings of the International Conference on Acoustics,Speech and Signal Processing (2008)

Deploying GOOG-411: Early Lessons in Data, Measurement, and Testing

Michiel Bacchiani , Francoise Beaufays , Johan Schalkwyk, Mike Schuster , Brian Strope

Proc. ICASSP (2008)

Retrieval and Browsing of Spoken Content

Ciprian Chelba , Timothy J. Hazen, Murat Saraçlar

Signal Processing Magazine, IEEE, vol. 25 (2008), pp. 39-49

Speech Recognition with Weighted Finite-State Transducers

Mehryar Mohri , Fernando C. N. Pereira , Michael Riley

Handbook on Speech Processing and Speech Communication, Part E: Speech recognition, Springer-Verlag, Heidelberg, Germany (2008)

Handbook on Speech Processing and Speech Communication, Part E: Speech recognition, Springer-Verlag, Heidelberg, Germany (2007)

• Shop Now: Amazon Labor Day Tech Sale
• Get These 12 Student Discounts!

How to Use Google's Text-to-Speech Feature on Android

Search the Settings app for Select to Speak to read text aloud with Google's TTS feature

• How to Use Select to Speak
• Managing the Options
• Translating Text
• Frequently Asked Questions

What to Know

• Open the Settings app and go to Accessibility > Select to Speak .
• Tap the toggle to turn it on, then tap Allow or OK to confirm permissions.
• Open any app, tap the Select to Speak shortcut, then tap an item to read it aloud. Tap Stop to end playback.

This article explains how to use the Google text-to-speech feature on Android so that you can have texts read out loud. It includes information on managing the language and voice used for reading text aloud. Instructions apply to Android 7 and up.

How to Use Google Text-to-Speech on Android

Several accessibility features are built into Android. If you want to hear text read aloud to you, use Select to Speak.

Swipe down from the top of the phone, then tap the gear icon to open the Settings app.

Tap Accessibility .

Tap Select to Speak .

If you don't see Select to Speak , tap Installed services to find it.

Tap the Select to Speak toggle switch to turn it on. On some phones, this is called Select to Speak shortcut .

Tap Allow or OK to confirm the permissions your phone needs to turn on this feature.

Open any app and tap the Select to Speak icon from the side of the screen.

Tap the Play icon to have your phone read everything on the screen, starting at the top. If you only want some text read aloud, trigger Select to Speak by tapping the floating icon, then tap the text.

Tap the left arrow next to the Play button to see more playback options.

Tap Stop to end playback.

Use TalkBack on your Android if you want spoken feedback as you use your device.

How to Manage Android Text-to-Speech Voices and Options

Android gives you some control over the language and voice used to read text aloud via Select to Speak. It's easy to change the language, accent, pitch, or speed of the synthesized text voice.

Go to Settings > General management > Language and input . Or on some devices, Settings > Languages .

Tap Text-to-speech or Text-to-speech output .

In the menu that appears, adjust the Speech rate and Pitch until it sounds the way you want.

To change the language, tap Language , then choose the language you want to hear when text is read aloud.

Use Select to Speak With Google Lens to Translate Written Words

Another way you can use this text-to-speech functionality is while translating languages. Google Lens is great for this. Just point the camera at some text you don't understand and it'll be translated into your language. Select to Speak can then read that aloud.

To turn off text-to-speech, go to Settings > Accessibility > Select to Speak and tap the toggle switch to turn it Off .

The Android text-to-speech feature works in the Google Docs app, but on a computer, you must download the Screen Reader extension for Chrome . Then, go to Tools > Accessibility settings > Turn on Screen Reader Support > OK , highlight the text, and select Accessibility > Speak > Speak selection .

To use voice typing in Google Docs , place your cursor in the document where you want to begin typing, then select Tools > Voice Typing . Alternatively, you can also use a keyboard shortcut Ctrl + Shift + S or Command + Shift + S .

Get the Latest Tech News Delivered Every Day

• How to Use Speech-to-Text on Android
• How to Use the Google Voice Recorder App on Android
• The Official Android Versions Guide: Everything You Need to Know
• How to Use Android 12's Adaptive Notifications Ranking
• Android 13: News, Release Date, and Features
• How to Make Your Android Phone Read Your Texts
• How to Change the Keyboard on Android
• How to Control F on Android
• What Is Android Dark Mode? And How to Enable It
• 9 Best Keyboards for Android in 2024
• How to Transfer Text Messages From Android to Android
• How to Connect a Phone to a TV Wirelessly
• What Is an Android Photo Sphere?
• All About the Gboard Keyboard for Android and iOS
• How to Make a Video Call on Android
• How to Check Your Data Usage

Jump to content

Our mission, products, and impact

More about our core commitments

Expanding what's possible for everyone

Unlocking opportunity with education & career tools

Keeping billions of people safe online

Helping people with information in critical moments

Committed to being carbon free by 2030

A sound idea

How Live Transcribe went from helping a team to communicate — to helping millions of people

3-minute read

“What jump-started Live Transcribe was one person caring about another person in the company and doing something about it.”

Eve Andersson, Accessibility & Disability Inclusion Director

Hear from Chet, Dimitri, Sagar, and other Googlers on how the team came together to launch Live Transcribe

Watch the Video

Produced in partnership with ATTN:, a media company creating purpose-driven stories

After decades of creating innovative solutions to communicate, Dimitri Kanevsky, who lost his hearing at an early age, worked with his Google teammates to create Live Transcribe — a speech-to-text mobile app that helps him engage with spoken words and surrounding sounds in real time. Today, after years of testing and refinement in collaboration with the deaf and hard of hearing community, this technology enables millions of people to be a part of every conversation.

Dimitri Kanevsky, a Speech Research Scientist at Google

Bridging the communication gap

“When Chet developed the prototype ... I told him, ‘I’ve been dreaming about this my whole life!’”

Dimitri Kanevsky, Speech Research Scientist

As a research scientist working to improve speech recognition accuracy, Dimitri joined Google in 2014. In meetings with colleagues, he used CART, a professional interpreter service that displays speech-to-text captions in real time on a dedicated monitor. Although it was helpful, CART required multiple devices and advance preparation. Communication with his team members — including engineer Chet Gnegy and product manager Sagar Savla — also happened through more improvised methods: using note-taking apps, passing sticky notes, even hand gestures.

This experience led Chet to test an idea. He knew that speech transcription accuracy had advanced significantly, thanks in large part to Dimitri’s contributions to the field. But was the technology good enough to capture and display conversations on a phone’s screen in real time? He built a rough prototype and gave it to Dimitri to pilot. “When Chet developed [it], there were a lot of transcript errors,” Dimitri recalls. “He would ask, ‘How can you use this?’ And I told him, ‘Are you kidding? I’ve been dreaming about this my whole life!’”

Dimitri using Live Transcribe during a video call with his family

Broadening the conversation

“There are millions of people in the world who are deaf — most who do not communicate in English or have means to use expensive captioning services. We had to find a way to not only make the technology available in many languages, but also to make it free.”

Sagar Savla, Product Manager for speech recognition products

Seeking additional input, Dimitri, Sagar, and Chet brought the prototype to an accessibility innovation sprint, where Google teams from around the world pitch new ideas and exchange feedback on accessibility products. After receiving enthusiastic internal support, Sagar knew that the app had the potential to help millions of people — including his grandmother, who is hard of hearing. With the help of Gallaudet University — the world’s foremost institution for the education of the deaf and hard of hearing — he led the team to turn the prototype into a publicly available product.

Sagar during a visit to Gallaudet University in Washington, D.C.

Live Transcribe launched in 2019, transcribing real-time speech in over 70 languages on Android and Chrome OS devices. A year later, the app was updated to also include notifications that alert users of critical sounds in one’s environment — a feature that helps not only people who are deaf or hard of hearing but also those who are unable to hear noises temporarily, such as when someone is wearing headphones.

The ideas don’t stop there: future enhancements include adding even more languages, increased transcription accuracy, and better experiences for those communicating across languages or in group settings. Downloaded over 100 million times as of 2021, Live Transcribe underscores the immense impact a single idea can have toward creating richer, more inclusive human connections.

“For the first time, I could speak with my granddaughters. It was amazing to talk to them, to play chess, to hear their stories.”

More on this topic

Hear from Eve Andersson and other accessibility leads on Google’s efforts to help everyone access and enjoy the web.

How Live Caption on the Google Pixel allows people who live with hearing loss to make phone calls.

Become a trusted tester for Project Relate, an Android app that aims to help people with speech impairments communicate more easily.

Our commitment to hiring people with disabilities.

Related stories.

How a committed team brought wildfire boundary maps to life

How Jason Barnes and others are leading the way to a more accessible world

Creating new opportunities for Black and disabled Black artists

Finding wheelchair accessible places on Google Maps

• Share - Email Share - Email
• Get shareable link Get shareable link
• Share - Facebook Share - Facebook
• Share - LinkedIn Share - LinkedIn
• Share - X Share - X

ConsumerSearch.com

• Home & Garden
• Fitness & Sports
• Family & Pets
• Health & Beauty

The Ultimate Guide to Google Speech to Text: How it Works and How to Use It

In today’s digital age, technology continues to advance at an unprecedented pace. One remarkable development that has gained significant attention is the ability of machines to convert spoken language into written text. This technology, known as speech-to-text, has revolutionized various industries and has become an essential tool for many individuals. Among the numerous providers of this service, Google stands out with its exceptional speech-to-text capabilities. In this ultimate guide, we will explore how Google Speech to Text works and how you can utilize it effectively.

I. What is Google Speech to Text?

Google Speech to Text is a cutting-edge cloud-based application programming interface (API) developed by Google. It leverages advanced machine learning algorithms to accurately transcribe spoken words into written text in real-time. This powerful technology enables businesses and individuals alike to convert audio recordings or live speech into written form effortlessly.

II. How Does Google Speech to Text Work?

Behind the scenes, Google Speech to Text relies on deep neural networks that have been trained on vast amounts of audio data from diverse sources. These neural networks are designed to recognize patterns in speech and convert them into text with remarkable accuracy.

When utilizing Google Speech to Text, users can send audio data in various formats such as WAV or FLAC files or even stream it directly from a microphone or other sources. The API then processes this data by breaking it down into smaller chunks called “frames.” Each frame is analyzed individually using complex algorithms that identify phonemes (distinct sounds) within the speech.

To improve accuracy further, the API also takes contextual information into account by analyzing adjacent frames and considering factors such as word probability and language models. Additionally, users have the option of specifying additional parameters such as language preferences or profanity filtering for better transcription results.

III. How Can You Use Google Speech to Text?

Transcription Services: One of the primary use cases for Google Speech to Text is transcription services. Content creators, journalists, and researchers can utilize this technology to convert interviews, podcasts, or other audio recordings into written form quickly and accurately. This not only saves time but also enhances accessibility by providing text-based content for individuals with hearing impairments.

Voice-Controlled Applications: Google Speech to Text can be integrated into various applications to enable voice-controlled functionalities. For example, it can be used in voice assistants or chatbots to process user commands and generate appropriate responses in real-time. This opens up endless possibilities for hands-free interactions and automation.

Data Analysis: Businesses can also leverage Google Speech to Text for data analysis purposes. By converting recorded customer service calls or meetings into text, companies can extract valuable insights through sentiment analysis, keyword extraction, or topic modeling. These insights can inform decision-making processes and help improve customer experiences.

Accessibility Solutions: Google Speech to Text plays a crucial role in making digital content more accessible for individuals with disabilities such as visual impairments or dyslexia. By converting spoken words into written text, it enables these individuals to consume information more effectively and participate fully in the digital world.

IV. Conclusion

Google Speech to Text is an advanced speech recognition technology that has transformed the way we interact with audio content. Its accuracy, speed, and versatility make it an invaluable tool across various industries and applications. Whether you need transcription services, voice-controlled applications, data analysis capabilities, or accessibility solutions – Google Speech to Text is a reliable choice that empowers users with cutting-edge speech-to-text functionality. With its continuous improvements driven by machine learning advancements, we can expect even greater accuracy and efficiency from this remarkable technology in the future.

In summary, Google Speech to Text offers a wide range of possibilities that enhance productivity and accessibility while revolutionizing our relationship with spoken language. Embrace this powerful tool today and unlock its potential in your personal or professional endeavors.

This text was generated using a large language model, and select text has been reviewed and moderated for purposes such as readability.

MORE FROM CONSUMERSEARCH.COM

• Español – América Latina
• Português – Brasil
• Tiếng Việt

Using the Speech-to-Text API with Python

1. overview.

The Speech-to-Text API enables developers to convert audio to text in over 125 languages and variants, by applying powerful neural network models in an easy to use API.

In this tutorial, you will focus on using the Speech-to-Text API with Python.

What you'll learn

• How to set up your environment
• How to transcribe audio files in English
• How to transcribe audio files with word timestamps
• How to transcribe audio files in different languages

What you'll need

• A Google Cloud project
• A browser, such as Chrome or Firefox
• Familiarity using Python

How will you use this tutorial?

How would you rate your experience with python, how would you rate your experience with google cloud services, 2. setup and requirements, self-paced environment setup.

• Sign-in to the Google Cloud Console and create a new project or reuse an existing one. If you don't already have a Gmail or Google Workspace account, you must create one .

• The Project name is the display name for this project's participants. It is a character string not used by Google APIs. You can always update it.
• The Project ID is unique across all Google Cloud projects and is immutable (cannot be changed after it has been set). The Cloud Console auto-generates a unique string; usually you don't care what it is. In most codelabs, you'll need to reference your Project ID (typically identified as PROJECT_ID ). If you don't like the generated ID, you might generate another random one. Alternatively, you can try your own, and see if it's available. It can't be changed after this step and remains for the duration of the project.
• For your information, there is a third value, a Project Number , which some APIs use. Learn more about all three of these values in the documentation .
• Next, you'll need to enable billing in the Cloud Console to use Cloud resources/APIs. Running through this codelab won't cost much, if anything at all. To shut down resources to avoid incurring billing beyond this tutorial, you can delete the resources you created or delete the project. New Google Cloud users are eligible for the $300 USD Free Trial program.

Start Cloud Shell

While Google Cloud can be operated remotely from your laptop, in this codelab you will be using Cloud Shell , a command line environment running in the Cloud.

Activate Cloud Shell

If this is your first time starting Cloud Shell, you're presented with an intermediate screen describing what it is. If you were presented with an intermediate screen, click Continue .

It should only take a few moments to provision and connect to Cloud Shell.

This virtual machine is loaded with all the development tools needed. It offers a persistent 5 GB home directory and runs in Google Cloud, greatly enhancing network performance and authentication. Much, if not all, of your work in this codelab can be done with a browser.

Once connected to Cloud Shell, you should see that you are authenticated and that the project is set to your project ID.

• Run the following command in Cloud Shell to confirm that you are authenticated:

Command output

• Run the following command in Cloud Shell to confirm that the gcloud command knows about your project:

If it is not, you can set it with this command:

3. Environment setup

Before you can begin using the Speech-to-Text API, run the following command in Cloud Shell to enable the API:

You should see something like this:

Now, you can use the Speech-to-Text API!

Navigate to your home directory:

Create a Python virtual environment to isolate the dependencies:

Activate the virtual environment:

Install IPython and the Speech-to-Text API client library:

Now, you're ready to use the Speech-to-Text API client library!

In the next steps, you'll use an interactive Python interpreter called IPython , which you installed in the previous step. Start a session by running ipython in Cloud Shell:

You're ready to make your first request...

4. Transcribe audio files

In this section, you will transcribe an English audio file.

Copy the following code into your IPython session:

Take a moment to study the code and see how it uses the recognize client library method to transcribe an audio file*.* The config parameter indicates how to process the request and the audio parameter specifies the audio data to be recognized.

Send a request:

You should see the following output:

Update the configuration to enable automatic punctuation and send a new request:

In this step, you were able to transcribe an audio file in English, using different parameters, and print out the result. You can read more about transcribing audio files .

5. Get word timestamps

Speech-to-Text can detect time offsets (timestamps) for the transcribed audio. Time offsets show the beginning and end of each spoken word in the supplied audio. A time offset value represents the amount of time that has elapsed from the beginning of the audio, in increments of 100ms.

To transcribe an audio file with word timestamps, update your code by copying the following into your IPython session:

Take a moment to study the code and see how it transcribes an audio file with word timestamps*.* The enable_word_time_offsets parameter tells the API to return the time offsets for each word (see the doc for more details).

In this step, you were able to transcribe an audio file in English with word timestamps and print the result. Read more about getting word timestamps .

6. Transcribe different languages

The Speech-to-Text API recognizes more than 125 languages and variants! You can find a list of supported languages here .

In this section, you will transcribe a French audio file.

To transcribe the French audio file, update your code by copying the following into your IPython session:

In this step, you were able to transcribe a French audio file and print the result. You can read more about the supported languages .

7. Congratulations!

You learned how to use the Speech-to-Text API using Python to perform different kinds of transcription on audio files!

To clean up your development environment, from Cloud Shell:

• If you're still in your IPython session, go back to the shell: exit
• Stop using the Python virtual environment: deactivate
• Delete your virtual environment folder: cd ~ ; rm -rf ./venv-speech

To delete your Google Cloud project, from Cloud Shell:

• Retrieve your current project ID: PROJECT_ID=$(gcloud config get-value core/project)
• Make sure this is the project you want to delete: echo $PROJECT_ID
• Delete the project: gcloud projects delete $PROJECT_ID
• Test the demo in your browser: https://cloud.google.com/speech-to-text
• Speech-to-Text documentation: https://cloud.google.com/speech-to-text/docs
• Python on Google Cloud: https://cloud.google.com/python
• Cloud Client Libraries for Python: https://github.com/googleapis/google-cloud-python

This work is licensed under a Creative Commons Attribution 2.0 Generic License.

Except as otherwise noted, the content of this page is licensed under the Creative Commons Attribution 4.0 License , and code samples are licensed under the Apache 2.0 License . For details, see the Google Developers Site Policies . Java is a registered trademark of Oracle and/or its affiliates.

• Genshin Impact
• Random Chat Apps
• Best Games Like Star Wars Outlaws
• Avatar: Realms Collide
• Sonic Rumble
• Restaurant Games

Speech Services by Google

Make your apps talk to you, get the latest version.

Jul 15, 2024

Speech Services by Google is an official app from Google that lets you make other apps on your Android device talk to you, dictating the text on the screen out loud.

It's important to keep in mind that Speech Services by Google is not compatible with all the apps available for Android – in fact it only works with a few. Among the most important, two are from Google: Google Play Books and Google Translator. The first lets you listen to all the books on your device, and the second lets you listen to the translations.

To activate the voice, you have to follow these steps: access the settings, choose Language and Text Input, and select Google's text-to-speech engine as your default.

Requirements (Latest version)

• Android 4.4 or higher required

Information about Speech Services by Google 103.12.8

Older versions

Rate this app.

Speech services by google 2023 need support android oreo+ have experience have mobile banking safety hacker security improvements

Really thank you very much to the designers of the application that does my business. I recommend it 💞💯%

make it lighter

not sociable offline

Google Text-to-speech..

Accessibility Apps

Text-to-speech apps, similar to speech services by google, discover tools apps.

Uptodown Turbo

Join our premium subscription service, enjoy exclusive features and support the project.

• Manage Account
• Press and Journal ePaper
• Evening Express ePaper
• Newsletters

Woman, 87, dies after Oban house blaze

Two fire services appliances were called to the home near the CalMac pier.

An 87-year-old woman has died following a house fire on Monday in Oban.

The woman was taken from the house on Railway Pier along with her two pets – but later died in hospital.

The Scottish Fire and Rescue Service confirmed that a joint investigation had been launched into the fire.

John Sweeney, the Scottish Fire and Rescue Service’s group commander said: “We were alerted at 9.33pm on Monday, 26 August, to reports of a dwelling fire near Railway Pier, Oban.

“Operations control mobilised two fire appliances to the area, where firefighters assisted in the removal of one woman and two pets from the property.

“The woman was transferred to hospital, but sadly she later passed away.”

He added: “Our thoughts are very much with her family, friends and the wider community at this difficult time.

“A joint investigation alongside Police Scotland is now ongoing.”

A Police Scotland spokesperson said: “Around 10.20pm on Monday, 26 August 2024, officers received a report of a fire at a property on Gallanach Road, Oban.

“An 87-year-old woman was taken to hospital, where she later died.

“The fire is not suspicious.”

Our reporters are working to bring you the latest updates on this developing story.

Please check back later for more and follow The Press and Journal on Facebook and online for breaking news.

More from Highlands & Islands

Millionaire Highland MSP's heifer has 'plenty of femininity'

Iain Maciver: New Gaelic scrabble board should make boring lessons a lot more fun

'I've never seen anything like this': Fairy Pools tour boss could axe Skye trips…

Victim's relief as Alness woman found guilty of historic child abuse

Body of man, 95, lay in Oban retirement flat 'for a month'

Wedding plans: Skye's Old Man of Storr set for bonanza as council cashes in…

Caithness businessman found guilty of rapes at High Court in Inverness

Black Isle doctor joins mission to boost Scottish farmers' wellbeing

Woman, 62, found with head injuries in the grounds of Macdonald Aviemore Resort

£2.35m Highland castle with TWO private islands hits the market

Conversation.

Comments are currently disabled as they require cookies and it appears you've opted out of cookies on this site. To participate in the conversation, please adjust your cookie preferences in order to enable comments.