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Digital Object Identifier (DOI) : 10.14569/IJACSA.2012.031115
Article Published in International Journal of Advanced Computer Science and Applications(IJACSA), Volume 3 Issue 11, 2012.
Abstract: This paper presents a Bangla (widely used as Bengali) automatic speech recognition system (ASR) by suppressing gender effects. Gender characteristic plays an important role on the performance of 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 the proposed method, we have designed a new ASR incorporating the Local Features (LFs) instead of standard mel frequency cepstral coefficients (MFCCs) as an acoustic feature for Bangla by suppressing the gender effects, which embeds three HMM-based classifiers for corresponding male, female and geneder-independent (GI) characteristics. In the experiments on Bangla speech database prepared by us, the proposed system has achieved a significant improvement of word correct rates (WCRs), word accuracies (WAs) and sentence correct rates (SCRs) in comparison with the method that incorporates Standard MFCCs.
B.K.M Mizanur Rahman, Bulbul Ahamed, Md. Asfak-Ur-Rahman, Khaled Mahmud and Mohammad Nurul Huda, “Gender Effect Canonicalization for Bangla ASR” International Journal of Advanced Computer Science and Applications(IJACSA), 3(11), 2012. http://dx.doi.org/10.14569/IJACSA.2012.031115