PUB - Publikationen an der Universität Bielefeld

For the modelling of speech rate variation in speech recognition many approaches have been suggested. However, the training of speech-rate dependent models has by far received most of the attention. In order to investigate problematic aspects related with the classification of the speech data which represents one of the major problems of these approaches extensive experiments were carried out on a German corpus of read speech. The results indicate that while the kind of the model-driven speech-rate measure is only of minor importance a data-driven classification of the speech data significantly improves the performance of rate-dependent models. Further results suggest a detailed modelling of speech rate based on more general models. This means that it might be possible to model speech rate adaptation by means of a transformation based on a continuous measure.