# Dynamic Speech Endpoint Detection with Regression Targets

arXiv: https://arxiv.org/abs/2210.14252 · HTML: https://mlsanity.com/paper/2210.14252/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Oct 2022.

## Built on by (most cited first, 4 of 4)

- [DuplexCascade: Full-Duplex Speech-to-Speech Dialogue with VAD-Free Cascaded ASR-LLM-TTS Pipeline and Micro-Turn Optimization](https://mlsanity.com/paper/2603.09180/) (2026)
- [BanglaTalk: Towards Real-Time Speech Assistance for Bengali Regional Dialects](https://mlsanity.com/paper/2510.06188/) (2025)
- [STEER: Semantic Turn Extension-Expansion Recognition for Voice Assistants](https://mlsanity.com/paper/2310.16990/) (2023)
- [Egocentric Speaker Classification in Child-Adult Dyadic Interactions: From Sensing to Computational Modeling](https://mlsanity.com/paper/2409.09340/) (2024)

## Abstract (arXiv)

Interactive voice assistants have been widely used as input interfaces in various scenarios, e.g. on smart homes devices, wearables and on AR devices. Detecting the end of a speech query, i.e. speech end-pointing, is an important task for voice assistants to interact with users. Traditionally, speech end-pointing is based on pure classification methods along with arbitrary binary targets. In this paper, we propose a novel regression-based speech end-pointing model, which enables an end-pointer to adjust its detection behavior based on context of user queries. Specifically, we present a pause modeling method and show its effectiveness for dynamic end-pointing. Based on our experiments with vendor-collected smartphone and wearables speech queries, our strategy shows a better trade-off between endpointing latency and accuracy, compared to the traditional classification-based method. We further discuss the benefits of this model and generalization of the framework in the paper.