首个面向泰米尔语电话对话的语义断句检测数据集与模型，提升语音助手响应准确率。
TamilEOT: A Dataset and Model for Semantic End-of-Turn Detection in Tamil Telephone Speech
- 基于真实通话构建1.8万条标注断句数据，用音频模型实现端到端断句检测
- 在未见数据上准确率达86.13%，推理速度低于150毫秒/次
- 开源全部数据、代码与负样本结果，适合南亚语言语音交互研究者
语音助手需在每次停顿判断用户是否结束发言。当前方法依赖固定静音超时，过短则打断对话，过长则增加等待。现有语义断句检测模型均未覆盖南印度语言。本文发布TamilEOT：从116段真实泰米尔语电话对话中提取的18,485条标注说话轮次边界，并提供两个基于Smart Turn v3微调的纯音频检测器。在30个未见通话的4,168个片段上，零样本准确率70.30%提升至83.71%（8.7 MB）和86.13%（21 MB），ROC-AUC由0.751升至0.921。两模型单线程运行于笔记本CPU下均小于150毫秒。规则标签人工验证准确率正类95.9%、负类44.4%（低于随机），因规则问题与模型目标不一致；改用音频大模型标注后人类一致性达97.5%，成本为5.69美元。所有实验变量中仅编码器容量影响性能；相同配置下三组重复结果波动仅0.87准确率点。将相同标注通过生产级语音活动检测器与流式适配器处理，额外损失2.60点性能，且7.8%的边界从未被模型捕捉。数据、权重、代码及所有负面结果均已公开。
原文摘要 · Abstract (English)
A voice agent has to decide, at every pause, whether the user has finished speaking. Without a model of the language that decision falls back to a fixed silence timeout: set it short and the agent interrupts, set it long and every turn pays the full wait. Open semantic end-of-turn detectors exist, but to our knowledge none covers a South Indian language. We release TamilEOT: 18,485 labelled turn boundaries cut from 116 real Tamil telephone conversations, and two audio-only detectors fine-tuned from Smart Turn v3. On a held-out split of 4,168 clips from 30 unseen calls, accuracy rises from 70.30% zero-shot to 83.71% (8.7 MB) and 86.13% (21 MB); ROC-AUC rises from 0.751 to 0.921. Both models run in under 150 ms single-threaded on a laptop CPU. We also report what building it cost. Rule-derived labels, checked against a blind human listening pass, were right 95.9% of the time on the positive class and 44.4% on the negative class, which is below chance, because the rule answered a different question than the model is asked. Replacing them with an audio-LLM labeller measured at 97.5% human agreement cost US$5.69. Of every training lever we measured, only encoder capacity moved the result; three runs at identical config and seed span 0.87 accuracy points, which is the floor below which none of our other deltas mean anything. Replaying the same labelled boundaries through the production VAD and streaming adapter costs a further 2.60 points, and 7.8% of boundaries are never surfaced to the model at all. Data, weights, code and every negative result are public.
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