Paper Info
Title
Personal VAD 2.0: Optimizing Personal Voice Activity Detection for On-Device Speech Recognition
Abstract
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 devices and smart home speakers. In this work, we present Personal VAD 2.0, a personalized voice activity detector that detects the voice activity of a target speaker, as part of a streaming on-device ASR system. Although previous proof-of-concept studies have validated the effectiveness of Personal VAD, there are still several critical challenges to address before this model can be used in production: first, the quality must be satisfactory in both enrollment and enrollment-less scenarios; second, it should operate in a streaming fashion; and finally, the model size should be small enough to fit a limited latency and CPU/Memory budget. To meet the multi-faceted requirements, we propose a series of novel designs: 1) advanced speaker embedding modulation methods; 2) a new training paradigm to generalize to enrollment-less conditions; 3) architecture and runtime optimizations for latency and resource restrictions. Extensive experiments on a realistic speech recognition system demonstrated the state-of-the-art performance of our proposed method.
Year
DOI
Venue
2022
10.21437/INTERSPEECH.2022-856
Conference of the International Speech Communication Association (INTERSPEECH)
DocType
Citations 
PageRank 
Conference
0
0.34
References 
Authors
0
8
Name
Order
Citations
PageRank
Shaojin Ding131.73
Rajeev Rikhye201.01
Qiao Liang37719.86
Yanzhang He46416.36
Quan Wang511520.15
Arun Narayanan642532.99
Tom O'Malley700.68
Ian McGraw825324.41