Search Results for author: Tara Sainath

Found 17 papers, 3 papers with code

Self-supervised Representation Learning for Speech Processing

1 code implementation NAACL (ACL) 2022 Hung-Yi Lee, Abdelrahman Mohamed, Shinji Watanabe, Tara Sainath, Karen Livescu, Shang-Wen Li, Shu-wen Yang, Katrin Kirchhoff

Due to the growing popularity of SSL, and the shared mission of the areas in bringing speech and language technologies to more use cases with better quality and scaling the technologies for under-represented languages, we propose this tutorial to systematically survey the latest SSL techniques, tools, datasets, and performance achievement in speech processing.

Representation Learning

Contextual Biasing with the Knuth-Morris-Pratt Matching Algorithm

no code implementations29 Sep 2023 Weiran Wang, Zelin Wu, Diamantino Caseiro, Tsendsuren Munkhdalai, Khe Chai Sim, Pat Rondon, Golan Pundak, Gan Song, Rohit Prabhavalkar, Zhong Meng, Ding Zhao, Tara Sainath, Pedro Moreno Mengibar

Contextual biasing refers to the problem of biasing the automatic speech recognition (ASR) systems towards rare entities that are relevant to the specific user or application scenarios.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +1

Massive End-to-end Models for Short Search Queries

no code implementations22 Sep 2023 Weiran Wang, Rohit Prabhavalkar, Dongseong Hwang, Qiujia Li, Khe Chai Sim, Bo Li, James Qin, Xingyu Cai, Adam Stooke, Zhong Meng, CJ Zheng, Yanzhang He, Tara Sainath, Pedro Moreno Mengibar

In this work, we investigate two popular end-to-end automatic speech recognition (ASR) models, namely Connectionist Temporal Classification (CTC) and RNN-Transducer (RNN-T), for offline recognition of voice search queries, with up to 2B model parameters.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +1

Augmenting conformers with structured state space models for online speech recognition

no code implementations15 Sep 2023 Haozhe Shan, Albert Gu, Zhong Meng, Weiran Wang, Krzysztof Choromanski, Tara Sainath

Online speech recognition, where the model only accesses context to the left, is an important and challenging use case for ASR systems.

speech-recognition Speech Recognition

A Comparison of Semi-Supervised Learning Techniques for Streaming ASR at Scale

no code implementations19 Apr 2023 Cal Peyser, Michael Picheny, Kyunghyun Cho, Rohit Prabhavalkar, Ronny Huang, Tara Sainath

Unpaired text and audio injection have emerged as dominant methods for improving ASR performance in the absence of a large labeled corpus.

Dual Learning for Large Vocabulary On-Device ASR

no code implementations11 Jan 2023 Cal Peyser, Ronny Huang, Tara Sainath, Rohit Prabhavalkar, Michael Picheny, Kyunghyun Cho

Dual learning is a paradigm for semi-supervised machine learning that seeks to leverage unsupervised data by solving two opposite tasks at once.

Unified End-to-End Speech Recognition and Endpointing for Fast and Efficient Speech Systems

no code implementations1 Nov 2022 Shaan Bijwadia, Shuo-Yiin Chang, Bo Li, Tara Sainath, Chao Zhang, Yanzhang He

In this work, we propose a method to jointly train the ASR and EP tasks in a single end-to-end (E2E) multitask model, improving EP quality by optionally leveraging information from the ASR audio encoder.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +1

Streaming End-to-End Multilingual Speech Recognition with Joint Language Identification

no code implementations13 Sep 2022 Chao Zhang, Bo Li, Tara Sainath, Trevor Strohman, Sepand Mavandadi, Shuo-Yiin Chang, Parisa Haghani

Language identification is critical for many downstream tasks in automatic speech recognition (ASR), and is beneficial to integrate into multilingual end-to-end ASR as an additional task.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +2

Echo State Speech Recognition

no code implementations18 Feb 2021 Harsh Shrivastava, Ankush Garg, Yuan Cao, Yu Zhang, Tara Sainath

We propose automatic speech recognition (ASR) models inspired by echo state network (ESN), in which a subset of recurrent neural networks (RNN) layers in the models are randomly initialized and untrained.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +1

Deep Learning for Audio Signal Processing

1 code implementation30 Apr 2019 Hendrik Purwins, Bo Li, Tuomas Virtanen, Jan Schlüter, Shuo-Yiin Chang, Tara Sainath

Given the recent surge in developments of deep learning, this article provides a review of the state-of-the-art deep learning techniques for audio signal processing.

Audio Signal Processing Automatic Speech Recognition +5

Lingvo: a Modular and Scalable Framework for Sequence-to-Sequence Modeling

2 code implementations21 Feb 2019 Jonathan Shen, Patrick Nguyen, Yonghui Wu, Zhifeng Chen, Mia X. Chen, Ye Jia, Anjuli Kannan, Tara Sainath, Yuan Cao, Chung-Cheng Chiu, Yanzhang He, Jan Chorowski, Smit Hinsu, Stella Laurenzo, James Qin, Orhan Firat, Wolfgang Macherey, Suyog Gupta, Ankur Bapna, Shuyuan Zhang, Ruoming Pang, Ron J. Weiss, Rohit Prabhavalkar, Qiao Liang, Benoit Jacob, Bowen Liang, HyoukJoong Lee, Ciprian Chelba, Sébastien Jean, Bo Li, Melvin Johnson, Rohan Anil, Rajat Tibrewal, Xiaobing Liu, Akiko Eriguchi, Navdeep Jaitly, Naveen Ari, Colin Cherry, Parisa Haghani, Otavio Good, Youlong Cheng, Raziel Alvarez, Isaac Caswell, Wei-Ning Hsu, Zongheng Yang, Kuan-Chieh Wang, Ekaterina Gonina, Katrin Tomanek, Ben Vanik, Zelin Wu, Llion Jones, Mike Schuster, Yanping Huang, Dehao Chen, Kazuki Irie, George Foster, John Richardson, Klaus Macherey, Antoine Bruguier, Heiga Zen, Colin Raffel, Shankar Kumar, Kanishka Rao, David Rybach, Matthew Murray, Vijayaditya Peddinti, Maxim Krikun, Michiel A. U. Bacchiani, Thomas B. Jablin, Rob Suderman, Ian Williams, Benjamin Lee, Deepti Bhatia, Justin Carlson, Semih Yavuz, Yu Zhang, Ian McGraw, Max Galkin, Qi Ge, Golan Pundak, Chad Whipkey, Todd Wang, Uri Alon, Dmitry Lepikhin, Ye Tian, Sara Sabour, William Chan, Shubham Toshniwal, Baohua Liao, Michael Nirschl, Pat Rondon

Lingvo is a Tensorflow framework offering a complete solution for collaborative deep learning research, with a particular focus towards sequence-to-sequence models.

Sequence-To-Sequence Speech Recognition

Bytes are All You Need: End-to-End Multilingual Speech Recognition and Synthesis with Bytes

no code implementations22 Nov 2018 Bo Li, Yu Zhang, Tara Sainath, Yonghui Wu, William Chan

We present two end-to-end models: Audio-to-Byte (A2B) and Byte-to-Audio (B2A), for multilingual speech recognition and synthesis.

speech-recognition Speech Recognition +1

Deep Neural Networks for Acoustic Modeling in Speech Recognition

no code implementations Signal Processing Magazine 2012 Geoffrey Hinton, Li Deng, Dong Yu, George Dahl, Abdel-rahman Mohamed, Navdeep Jaitly, Andrew Senior, Vincent Vanhoucke, Patrick Nguyen, Tara Sainath, Brian Kingsbury

Most current speech recognition systems use hidden Markov models (HMMs) to deal with the temporal variability of speech and Gaussian mixture models to determine how well each state of each HMM fits a frame or a short window of frames of coefficients that represents the acoustic input.

speech-recognition Speech Recognition

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