Gender factor suppression for Bangla ASR
Foyzul Hassan, Mohammed Rokibul Alam Kotwal, Suman Ahmmed, Bkm Mizanur Rahman, Tasneem Halim, Mohammad Nurul Huda · International journal of tomography and simulation · 2013
Hidden factor such as gender characteristic plays an important role on the performance of Bangla (widely used as Bengali) automatic speech recognition (ASR). If there is a 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 our previous paper, we proposed a technique for gender effects suppression that composed two hidden Markov model (HMM)-based classifiers that focused on the gender factor. In the current study, we utilize three HMM-based classifiers corresponding to male, female and gender-independent (GI) characteristics. In an experiment on a collected Bangla speech database, the proposed system that incorporates GI-classifier has achieved a significant improvement over prior art in terms of both word and sentence accuracy. Moreover, the proposed system requires fewer mixture components in HMM and hence, reduces computation time.