语音体裁对 End-of-turn 检测器训练的重要性
The Importance of Speech Genre for End-of-Turn Detector Training
AI 导读
一项 arXiv 研究通过可复现标注流程构建了多人多轮对话与单人单轮人机两组匹配语料，并用同一 streaming ConvLSTM 模型分别训练 End-of-turn（EoT）检测器。
正文 · 原文
Abstract:End-of-turn (EoT) detection has been studied mostly through features, models, and modalities, while the speech genre of the training data has drawn far less attention. We isolate this factor. Using one reproducible annotation pipeline, we build a matched pair of corpora, multi-turn human-human conversational and single-turn human-machine, and train the same streaming ConvLSTM EoT detector on each. Across four test sets the conversational model reaches up to +0.45 higher F1, driven by fewer false alarms on the mid-turn pauses and hesitations that are rare in single-turn corpora. This conversational-training effect is not specific to frame-level decoding. Under a matched complete-or-not test it also appears at the utterance level, across two encoders, but only on conversational test data. Testing only on single-turn speech, as most prior work does, therefore hides the benefit of conversational training data. Against open-source baselines, our 1.8M-parameter model attains the highest F1 on every test set.
| Subjects: | Audio and Speech Processing (eess.AS); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.39742 [eess.AS] |
| (or arXiv:2610.39742v1 [eess.AS] for this version) | |
| https://doi.org/10.48550/arXiv.2610.39742 arXiv-issued DOI via DataCite |
Submission history
From: Rini Sharon [view email]
[v1]
Thu, 29 Oct 2026 10:05:07 UTC (167 KB)
来源：arXiv cs.AI · arxiv.org