Speaking-rate dependent decoding and adaptation for spontaneous lecture speech recognition

This paper addresses the problem of speaking rate in large vocabulary spontaneous speech recognition. In spontaneous lecture speech, the speaking rate is generally fast and may vary a lot within a talk. We also observed different error tendencies for fast and slow speech segments. Therefore, we first present a speaking-rate dependent decoding strategy that applies the most adequate acoustic analysis, phone models and decoding parameters according to the speaking rate. Several methods are investigated and their selective application leads to accuracy improvement. We also propose to make use of speaking-rate information in speaker adaptation, in which the different adapted models are set up for fast and slow utterances. It is confirmed that the method is more effective than normal adaptation.