Robust speech detection method for telephone speech recognition system

This paper describes speech endpoint detection methods for continuous speech recognition systems used over telephone networks. Speech input to these systems may be contaminated not only by various ambient noises but also by various irrelevant sounds generated by users such as coughs, tongue clicking, lip noises and certain out-of-task utterances. Under these adverse conditions, robust speech endpoint detection remains an unsolved problem. We found in fact, that speech endpoint detection errors occurred in over 10% of the inputs in field trials of a voice activated telephone extension system. These errors were caused by problems of (1) low SNR, (2) long pauses between phrases and (3) irrelevant sounds prior to task sentences. To solve the first two problems, we propose a real-time speech ending point detection algorithm based on the implicit approach, which finds a sentence end by comparing the likelihood of a complete sentence hypothesis and other hypotheses. For the third problem, we propose a speech beginning point detection algorithm which rejects irrelevant sounds by using likelihood ratio and duration conditions. The effectiveness of these methods was evaluated under various conditions. As a result, we found that the ending point detection algorithm was not affected by long pauses and that the beginning point detection algorithm successfully rejected irrelevant sounds by using phone HMMs that fit the task. Furthermore, a garbage model of irrelevant sounds was also evaluated and we found that the garbage modeling technique and the proposed method compensated each other in their respective weak points and that the best recognition accuracy was achieved by integrating these methods.