Two-pass Endpoint Detection for Speech Recognition

Author:

Anirudh Raju, Aparna Khare, Di He, Ilya Sklyar, Long Chen, Sam Alptekin, Viet Anh Trinh, Zhe Zhang, Colin Vaz, Venkatesh Ravichandran, Roland Maas, Ariya Rastrow

Keyword:

Electrical Engineering and Systems Science, Audio and Speech Processing, Audio and Speech Processing (eess.AS), Sound (cs.SD)

journal:

date:

2024-01-17 00:00:00

Abstract

Endpoint (EP) detection is a key component of far-field speech recognition systems that assist the user through voice commands. The endpoint detector has to trade-off between accuracy and latency, since waiting longer reduces the cases of users being cut-off early. We propose a novel two-pass solution for endpointing, where the utterance endpoint detected from a first pass endpointer is verified by a 2nd-pass model termed EP Arbitrator. Our method improves the trade-off between early cut-offs and latency over a baseline endpointer, as tested on datasets including voice-assistant transactional queries, conversational speech, and the public SLURP corpus. We demonstrate that our method shows improvements regardless of the first-pass EP model used.

PDF: Two-pass Endpoint Detection for Speech Recognition.pdf