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

no code implementations • 1 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

Incremental Layer-wise Self-Supervised Learning for Efficient Speech Domain Adaptation On Device

no code implementations • 1 Oct 2021 • Zhouyuan Huo, Dongseong Hwang, Khe Chai Sim, Shefali Garg, Ananya Misra, Nikhil Siddhartha, Trevor Strohman, Françoise Beaufays

These models are typically trained on the server using transcribed speech data.

Domain Adaptation Self-Supervised Learning +2

Improving Streaming Automatic Speech Recognition With Non-Streaming Model Distillation On Unsupervised Data

no code implementations • 22 Oct 2020 • Thibault Doutre, Wei Han, Min Ma, Zhiyun Lu, Chung-Cheng Chiu, Ruoming Pang, Arun Narayanan, Ananya Misra, Yu Zhang, Liangliang Cao

We propose a novel and effective learning method by leveraging a non-streaming ASR model as a teacher to generate transcripts on an arbitrarily large data set, which is then used to distill knowledge into streaming ASR models.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +1

Toward domain-invariant speech recognition via large scale training

no code implementations • 16 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 Automatic Speech Recognition (ASR) +1