Predicting Barge-in Utterance Errors by using Implicitly-Supervised ASR Accuracy and Barge-in Rate per User
Predicting Barge-in Utterance Errors by using Implicitly-Supervised ASR Accuracy and Barge-in Rate per User
复制标题
使用隐式监督的 ASR 准确性和每个用户的打断率来预测打断话语错误
DOI：
10.3115/1667583.1667612
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发表时间：
2009
期刊：
影响因子：
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通讯作者：
Alexander I. Rudnicky
中科院分区：
文献类型：
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作者：
Kazunori Komatani;Alexander I. Rudnicky
单个用户的建模是一种很有前途的方法，可以改善为公众部署并重复使用的语音对话系统的性能。我们根据系统明确确认后的响应来定义每个用户的“隐式监督”ASR准确性。我们将估计的ASR准确度与用户的适应率（表示用户习惯使用系统的程度）相结合，以预测适应话语中的口译错误。实验结果表明，估计的ASR精度提高了预测性能。由于这种ASR精度和驳船率在运行时可获得，因此它们提高了预测性能，而无需手动标记。
Modeling of individual users is a promising way of improving the performance of spoken dialogue systems deployed for the general public and utilized repeatedly. We define "implicitly-supervised" ASR accuracy per user on the basis of responses following the system's explicit confirmations. We combine the estimated ASR accuracy with the user's barge-in rate, which represents how well the user is accustomed to using the system, to predict interpretation errors in barge-in utterances. Experimental results showed that the estimated ASR accuracy improved prediction performance. Since this ASR accuracy and the barge-in rate are obtainable at runtime, they improve prediction performance without the need for manual labeling.
使用基于 GMM 拒绝非预期输入的 Noice 鲁棒现实世界口语对话系统
DOI：
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发表时间：
2004
期刊：
Proceedings of 8th International Conference on Spoken Language Processing (ICSLP2004) TuA1302p-2,Vol.I
影响因子：
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作者：
Akinobu Lee;Keisuke Nakamura;Ryuichi Nisimura;Hiroshi Saruwatari;Kiyohiro Shikano
通讯作者：
Kiyohiro Shikano