Gender independent Bangla automatic speech recognition

Abstract

Speaker-specific characteristics play an important role on the performance of Bangla (widely used as Bengali) automatic speech recognition (ASR). Gender factor shows adverse effect in the classifier while recognizing a speech by an opposite gender, such as, training a classifier by male but testing is done by female or vice-versa. To obtain a robust ASR system in practice it is necessary to invent a system that incorporates gender independent effect for particular gender. In this paper, we have proposed a Gender-Independent technique for ASR that focused on a gender factor. The proposed method trains the classifier with the both types of gender, male and female, and evaluates the classifier for the male and female. For the experiments, we have designed a medium size Bangla (widely known as Bengali) speech corpus for both the male and female. The proposed system has showed a significant improvement of word correct rates, word accuracies and sentence correct rates in comparison with the method that suffers from gender effects. Moreover, it requires a fewer mixture component in hidden Markov model (HMMs) and hence, computation time.