184 citations · 252 across the 19 of their papers we have counts for
11 papers · 1 filter
Massive End-to-end Models for Short Search Queries
Weiran Wang, Rohit Prabhavalkar, Dongseong Hwang +11
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 of…
Personal VAD 2.0: Optimizing Personal Voice Activity Detection for On-Device Speech Recognition
Shaojin Ding, Rajeev Rikhye, Qiao Liang +5
Personalization of on-device speech recognition (ASR) has seen explosive growth in recent years, largely due to the increasing popularity of personal assistant features on mobile d…
4-bit Conformer with Native Quantization Aware Training for Speech Recognition
Shaojin Ding, Phoenix Meadowlark, Yanzhang He +3
Reducing the latency and model size has always been a significant research problem for live Automatic Speech Recognition (ASR) application scenarios. Along this direction, model qu…
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…
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…
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…
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…
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…
VoiceFilter-Lite: Streaming Targeted Voice Separation for On-Device Speech Recognition
Quan Wang, Ignacio Lopez Moreno, Mert Saglam +8
We introduce VoiceFilter-Lite, a single-channel source separation model that runs on the device to preserve only the speech signals from a target user, as part of a streaming speec…
Parallel Rescoring with Transformer for Streaming On-Device Speech Recognition
Wei Li, James Qin, Chung-Cheng Chiu +2
Recent advances of end-to-end models have outperformed conventional models through employing a two-pass model. The two-pass model provides better speed-quality trade-offs for on-de…
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…