Gender Effects Suppression in Bangla ASR by Designing Multiple HMM-Based Classifiers

Abstract

Speaker-specific characteristics play an important role on the performance of Bangla (widely used as Bengali) automatic speech recognition (ASR). It is difficult to recognize speech affected by gender factors, especially when an ASR system contains only a single acoustic model. If there exists any suppression process that represses the decrease of differences in acoustic-likelihood among categories resulted from gender factors, a robust ASR system can be realized. In this paper, we have proposed a technique of gender effects suppression that composed of two hidden Markov model (HMM)-based classifiers and that focused on a gender factor. In an experiment on Bangla speech database prepared by us, the proposed system has provided a significant improvement of word correct rate, word accuracy and sentence correct rate in comparison with the method that incorporates only a single HMM-based classifier for both male and female speakers.