时序瓶颈：为何时序与重叠对会话式用户界面、语音识别和对话系统至关重要
The timing bottleneck: Why timing and overlap are mission-critical for conversational user interfaces, speech recognition and dialogue systems
AI总结：
该研究指出时序是对话系统的关键要素，通过评估5款商用ASR系统发现6种语言会话语料词错误率差、说话人重叠影响大，且会波及下游意图识别，为会话式语音技术优化指明方向。
摘要：
语音识别系统是语音驱动人机交互中的关键中介。尽管语音识别在纯净的单声道音频上表现良好，但在开放式交互场景的实际用例中仍存在诸多挑战。我们认为时序对对话系统至关重要，并评估了5款主流商用ASR（自动语音识别）系统的会话支持与多语言支持能力。我们发现，6种语言的自然会话语料词错误率仍极差，且说话人重叠仍是核心挑战（研究1）。这一问题尤其会影响会话词汇的识别（研究2），进而对下游意图识别造成严重后果（研究3）。我们的研究结果有助于评估会话式ASR的当前发展水平，为多维错误分析与评估提供支撑，并明确了构建鲁棒交互式语音技术过程中最需关注的现象。
英文摘要：
Speech recognition systems are a key intermediary in voice-driven human-computer interaction. Although speech recognition works well for pristine monologic audio, real-life use cases in open-ended interactive settings still present many challenges. We argue that timing is mission-critical for dialogue systems, and evaluate 5 major commercial ASR systems for their conversational and multilingual support. We find that word error rates for natural conversational data in 6 languages remain abysmal, and that overlap remains a key challenge (study 1). This impacts especially the recognition of conversational words (study 2), and in turn has dire consequences for downstream intent recognition (study 3). Our findings help to evaluate the current state of conversational ASR, contribute towards multidimensional error analysis and evaluation, and identify phenomena that need most attention on the way to build robust interactive speech technologies.
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