Speaking-Rate Adaptation of Automatic Speech Recognition System through Fuzzy Classification based Time-Scale Modification

Abstract

In this paper, we study the role of speaking-rate adaptation (SRA) of automatic speech recognition (ASR) systems. The performance of an ASR system is reported to degrade when the speaking-rate is either too fast or too slow. In order to simulate such a situation, an ASR system was trained on adults' speech and used for transcribing speech data from adult as well as child speakers. Earlier studies have shown that, speaking-rate is significantly lower in the case of children when compared to adults. Consequently, the recognition performance for children's speech was noted to be very poor in contrast to adults' speech. To improve the recognition performance with respect to children's speech, speaking-rate was explicitly changed using time-scale modification (TSM). A recently proposed TSM approach based on fuzzy classification of spectral bins has been explored in this regard. The fuzzy-classification-based TSM technique is reported to be superior to state-of-the-art approaches. Effectiveness of the said TSM technique has not been studied yet in the context of ASR. The experimental studies presented in this paper show that SRA based on fuzzy classification results in a relative improvement of 30% over the baseline.