Unvoiced Landmark Detection for Segment-based Mandarin Continuous Speech Recognition

This paper presents an attempt to introduce unvoiced landmarks into statistical continuous speech recognition system. The unvoiced landmark detection algorithm proposed here locates the points in speech where the vocal folds stop or begin freely vibrating. In our experiments, 87.47% of stops and 98.94% of fricatives are segmented from speech after the unvoiced landmark detection, with a very low insertion error rate of 0.13%. Then these landmarks are incorporated into decoding process of segment model based recognizer as search beginning indicators. The effectiveness of landmark detection algorithm is verified in our landmark-guided recognition system with 240 sentences in 863Test database.