# Two-pass Endpoint Detection for Speech Recognition

arXiv: https://arxiv.org/abs/2401.08916 · HTML: https://mlsanity.com/paper/2401.08916/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Jan 2024.

## Built on by (most cited first, 1 of 1)

- [DualTurn: Learning Turn-Taking from Dual-Channel Generative Speech Pretraining](https://mlsanity.com/paper/2603.08216/) (2026)

## Abstract (arXiv)

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.