tailieunhanh - Báo cáo khoa học: "Predicting Barge-in Utterance Errors by using Implicitly Supervised ASR Accuracy and Barge-in Rate per User"

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. | Predicting Barge-in Utterance Errors by using Implicitly Supervised ASR Accuracy and Barge-in Rate per User Kazunori Komatani Graduate School of Informatics Kyoto University Yoshida Sakyo Kyoto 606-8501 Japan komatani@ Alexander I. Rudnicky Computer Science Department Carnegie Mellon University Pittsburgh PA 15213 . air@ Abstract 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. 1 Introduction The automatic speech recognition ASR result is the most important input information for spoken dialogue systems and therefore its errors are critical problems. Many researchers have tackled this problem by developing ASR confidence measures based on utterance-level information and dialogue-level information Litman et al. 1999 Walker et al. 2000 . Especially in systems deployed for the general public such as those of Ko-matani et al. 2005 and Raux et al. 2006 the systems need to correctly detect interpretation errors caused by various utterances made by various kinds of users including novices. Furthermore since some users access such systems repeatedly Komatani et al. 2007 error detection by using individual user models would be a promising way of improving performance. In another aspect in dialogue systems certain dialogue patterns indicate that ASR results in certain positions are .

• Báo cáo khoa học
• Predicting Barge in Utterance Errors
• Implicitly Supervised ASR Accuracy
• Barge in Rate per User
• báo cáo khoa học
• báo cáo ngôn ngữ
• xử lý ngôn ngữ tự nhiên
• Bài giảng Xử lý ngôn ngữ tự nhiên
• Ngôn ngữ tự nhiên
• Kỹ thuật lập trình
• Xử lý nhập nhằng ngữ nghĩa
• Xử lý ngữ nghĩa
• Xử lý ngôn ngữ
• Natural Language Processing
• Ngôn ngữ lập trình
• Nhập nhằng cấu trúc
• Tri thức về ngôn ngữ
• Mô hình ngôn ngữ
• Mô hình n gram
• Dịch máy
• Phương pháp dịch máy
• Hiểu ngôn ngữ
• Phân tích ngữ nghĩa
• Phân loại tin tự động
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• Thuộc tính về sự kiện
• Thuộc tính về sự kiện
• Mô hình ngôn ngữ Google Book N grams
• Mô hình ngôn ngữ KenLM
• Phương pháp làm mịn
• Tách từ tiếng Việt
• Gán nhãn từ loại
• Phân tích cú pháp
• Nghĩa từ vựng
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