Search Results for author: Khe Chai Sim

Found 17 papers, 1 papers with code

Pseudo Label Is Better Than Human Label

no code implementations22 Mar 2022 Dongseong Hwang, Khe Chai Sim, Zhouyuan Huo, Trevor Strohman

State-of-the-art automatic speech recognition (ASR) systems are trained with tens of thousands of hours of labeled speech data.

Automatic Speech Recognition

Joint Unsupervised and Supervised Training for Multilingual ASR

no code implementations15 Nov 2021 Junwen Bai, Bo Li, Yu Zhang, Ankur Bapna, Nikhil Siddhartha, Khe Chai Sim, Tara N. Sainath

Our average WER of all languages outperforms average monolingual baseline by 33. 3%, and the state-of-the-art 2-stage XLSR by 32%.

Masked Language Modeling Speech Recognition +1

Fast Contextual Adaptation with Neural Associative Memory for On-Device Personalized Speech Recognition

no code implementations5 Oct 2021 Tsendsuren Munkhdalai, Khe Chai Sim, Angad Chandorkar, Fan Gao, Mason Chua, Trevor Strohman, Françoise Beaufays

Fast contextual adaptation has shown to be effective in improving Automatic Speech Recognition (ASR) of rare words and when combined with an on-device personalized training, it can yield an even better recognition result.

Automatic Speech Recognition

Large-scale ASR Domain Adaptation using Self- and Semi-supervised Learning

no code implementations1 Oct 2021 Dongseong Hwang, Ananya Misra, Zhouyuan Huo, Nikhil Siddhartha, Shefali Garg, David Qiu, Khe Chai Sim, Trevor Strohman, Françoise Beaufays, Yanzhang He

Self- and semi-supervised learning methods have been actively investigated to reduce labeled training data or enhance the model performance.

Domain Adaptation

On-Device Personalization of Automatic Speech Recognition Models for Disordered Speech

no code implementations18 Jun 2021 Katrin Tomanek, Françoise Beaufays, Julie Cattiau, Angad Chandorkar, Khe Chai Sim

While current state-of-the-art Automatic Speech Recognition (ASR) systems achieve high accuracy on typical speech, they suffer from significant performance degradation on disordered speech and other atypical speech patterns.

Automatic Speech Recognition

An Investigation Into On-device Personalization of End-to-end Automatic Speech Recognition Models

no code implementations14 Sep 2019 Khe Chai Sim, Petr Zadrazil, Françoise Beaufays

Speaker-independent speech recognition systems trained with data from many users are generally robust against speaker variability and work well for a large population of speakers.

Automatic Speech Recognition

Toward domain-invariant speech recognition via large scale training

no code implementations16 Aug 2018 Arun Narayanan, Ananya Misra, Khe Chai Sim, Golan Pundak, Anshuman Tripathi, Mohamed Elfeky, Parisa Haghani, Trevor Strohman, Michiel Bacchiani

More importantly, such models generalize better to unseen conditions and allow for rapid adaptation -- we show that by using as little as 10 hours of data from a new domain, an adapted domain-invariant model can match performance of a domain-specific model trained from scratch using 70 times as much data.

Automatic Speech Recognition

Understanding Recurrent Neural State Using Memory Signatures

no code implementations11 Feb 2018 Skanda Koppula, Khe Chai Sim, Kean Chin

We demonstrate this method's usefulness in revealing information divergence in the bases of recurrent factorized kernels, visualizing the character-level differences between the memory of n-gram and recurrent language models, and extracting knowledge of history encoded in the layers of grapheme-based end-to-end ASR networks.

Multi-Dialect Speech Recognition With A Single Sequence-To-Sequence Model

no code implementations5 Dec 2017 Bo Li, Tara N. Sainath, Khe Chai Sim, Michiel Bacchiani, Eugene Weinstein, Patrick Nguyen, Zhifeng Chen, Yonghui Wu, Kanishka Rao

Sequence-to-sequence models provide a simple and elegant solution for building speech recognition systems by folding separate components of a typical system, namely acoustic (AM), pronunciation (PM) and language (LM) models into a single neural network.

Speech Recognition

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