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arXiv · 2506.07081

Streaming Endpointer for Spoken Dialogue using Neural Audio Codecs and Label-Delayed Training

Sathvik Udupa·Shinji Watanabe·Petr Schwarz·Jan Cernocky
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Abstract

Accurate, low-latency endpointing is crucial for effective spoken dialogue systems. While traditional endpointers often rely on spectrum-based audio features, this work proposes real-time speech endpointing for multi-turn dialogues using streaming, low-bitrate Neural Audio Codec (NAC) features, building upon recent advancements in neural audio codecs. To further reduce cutoff errors, we introduce a novel label delay training scheme. At a fixed median latency of 160 ms, our combined NAC and label delay approach achieves significant relative cutoff error reductions: 42.7% for a single-stream endpointer and 37.5% for a two-stream configuration, compared to baseline methods. Finally, we demonstrate efficient integration with a codec-based pretrained speech large language model, improving its median response time by 1200 ms and reducing its cutoff error by 35%.

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Sathvik Udupa, Shinji Watanabe, Petr Schwarz, Jan Cernocky. 2025-06-08. Streaming Endpointer for Spoken Dialogue using Neural Audio Codecs and Label-Delayed Training. https://arxiv.org/abs/2506.07081

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