Out-of-Task Utterance Detection Based on Bag-of-Words Using Automatic Speech Recognition Results
Example-based question answering (QA) is an effective approach for real-world spoken dialogue systems. A limitation of an example-based QA is that a system cannot appropriately respond to a user’s question, if a similar questionanswer pair does not exist in the question and answer database (QADB). For a robust spoken dialogue system, it is important to classify if a user’s utterance is in the task or out of the task. In this paper, we describe our approach for out-of-task utterance (OOT) detection. Using the Support Vector Machines (SVM), the detection model is trained with the bag of words from the 10-best automatic speech recognition (ASR) results. The number of words in a question, the number of unknown words, and the maximum similarity score against QADB are also used as features for the OOT detection. We apply our detection model to the Takemaru-kun dialogue system. We evaluate our detection model using adult’s utterances of two years and child’s utterances of one year spoken to Takemaru-kun. Our proposed method decreases the Equal Error Rate (EER) using speech recognition results by 4.4％ (from 21.3％ to 16.9％) in adult’s speech and by 3.6％ (from 31.8％ to 28.2％) in child’s speech, compared with the baseline method.
Yoko Fujita Shota Takeuchi Hiromichi Kawanami Tomoko Matsui Hiroshi Saruwatari Kiyohiro Shikano
Graduate School of Information Science, Nara Institute of Science and Technology, Japan Department of Statistical Modeling, The Institute of Statistical Mathematics, Japan
国际会议
2011亚太信号与信息处理协会年度峰会(APSIPAASC 2011)
西安
英文
1-4
2011-10-18（万方平台首次上网日期，不代表论文的发表时间）