An understanding strategy based on plausibility score in recognition history using CSR confidence measure
An understanding strategy based on plausibility score in recognition history using CSR confidence measure
复制标题
基于使用 CSR 置信度测量的识别历史中的合理性得分的理解策略
DOI：
10.21437/interspeech.2004-227
复制
发表时间：
2004
期刊：
影响因子：
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通讯作者：
Tatsuhiro Konishi
中科院分区：
文献类型：
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作者：
Toshihiko Itoh;A. Kai;Y. Itoh;Tatsuhiro Konishi
虽然汽车导航系统作为语音对话界面之一吸引了人们的注意，但由于自然语音和周围噪声的干扰而导致的识别错误可能会阻止流畅的对话并使用户失望。因此，本研究的目的在于建构一个能达到顺畅对话与高使用者满意度的对话系统。我们的系统通过使用基于连续语音识别器（CSR）和识别历史的置信度（CM）来执行语言理解和响应生成。本文介绍了对话系统中的口语理解技术。CM与语音类型和识别历史一起用于生成综合得分。该系统实现了口语理解，这对于给定的对话更合理。作为评估实验的结果，它表明，我们的系统是更有效的（超过15%）比语言理解技术，简单地优先考虑语音识别结果的高阶假设（n-best）。
Although car-navigation systems attract attention as one of spoken dialogue interfaces, recognition errors due to the in fl uence of natural speech and surrounding noise may prevent a smooth dialogue and disappoint the user. Thus, this research aims at the construction of a dialogue system which can achieve a smooth dialogue and a high degree of user satisfaction. Our system performs language understanding and response generation by using the con fi dence measure(CM) based on continuous speech recognizer(CSR) and the recognition history. This paper shows the spo-kenlanguageunderstanding technique in the dialogue system. The CM, together with the speech type and the recognition history, is used for generating an integrated score. The system realizes a spoken language understanding which is more plausible for a given dialogue. As the result of evaluation experiment, it was shown that our system is more ef fi cient (more than 15%) than a language understanding technique which simply gives priority to the higher-rank hypothesis of a speech recognition result (n-best).