activity
20172022
most citedLingvo: a Modular and Scalable Framework for Sequence-to-Sequence Modeling

184 citations · 209 across the 11 of their papers we have counts for

collaborators

15 papers

eess.AS2022

Closing the Gap between Single-User and Multi-User VoiceFilter-Lite

Rajeev Rikhye, Quan Wang, Qiao Liang +2

VoiceFilter-Lite is a speaker-conditioned voice separation model that plays a crucial role in improving speech recognition and speaker verification by suppressing overlapping speec…

eess.AS20212 cited

Learning Word-Level Confidence For Subword End-to-End ASR

David Qiu, Qiujia Li, Yanzhang He +9

We study the problem of word-level confidence estimation in subword-based end-to-end (E2E) models for automatic speech recognition (ASR). Although prior works have proposed trainin…

cs.CL2020

Less Is More: Improved RNN-T Decoding Using Limited Label Context and Path Merging

Rohit Prabhavalkar, Yanzhang He, David Rybach +4

End-to-end models that condition the output label sequence on all previously predicted labels have emerged as popular alternatives to conventional systems for automatic speech reco…

eess.AS20202 cited

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…

eess.AS2020

Confidence Estimation for Attention-based Sequence-to-sequence Models for Speech Recognition

Qiujia Li, David Qiu, Yu Zhang +5

For various speech-related tasks, confidence scores from a speech recogniser are a useful measure to assess the quality of transcriptions. In traditional hidden Markov model-based…

eess.AS2020

FastEmit: Low-latency Streaming ASR with Sequence-level Emission Regularization

Jiahui Yu, Chung-Cheng Chiu, Bo Li +8

Streaming automatic speech recognition (ASR) aims to emit each hypothesized word as quickly and accurately as possible. However, emitting fast without degrading quality, as measure…