859 citations · 1.2k across the 11 of their papers we have counts for
4 papers · 1 filter
A Better and Faster End-to-End Model for Streaming ASR
Bo Li, Anmol Gulati, Jiahui Yu +12
End-to-end (E2E) models have shown to outperform state-of-the-art conventional models for streaming speech recognition [1] across many dimensions, including quality (as measured by…
Dual-mode ASR: Unify and Improve Streaming ASR with Full-context Modeling
Jiahui Yu, Wei Han, Anmol Gulati +5
Streaming automatic speech recognition (ASR) aims to emit each hypothesized word as quickly and accurately as possible, while full-context ASR waits for the completion of a full sp…
Towards Fast and Accurate Streaming End-to-End ASR
Bo Li, Shuo-yiin Chang, Tara N. Sainath +4
End-to-end (E2E) models fold the acoustic, pronunciation and language models of a conventional speech recognition model into one neural network with a much smaller number of parame…
Improved Noisy Student Training for Automatic Speech Recognition
Daniel S. Park, Yu Zhang, Ye Jia +5
Recently, a semi-supervised learning method known as "noisy student training" has been shown to improve image classification performance of deep networks significantly. Noisy stude…