Gender Effect Canonicalization for Bangla ASR

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.

@article{Rahman2012,
  title     = {Gender Effect Canonicalization for Bangla ASR},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {3},
  number    = {11},
  year      = {2012},
  publisher = {The Science and Information Organization},
  author    = {B.K.M Mizanur Rahman and Bulbul Ahamed and Md. Asfak-Ur-Rahman and Khaled Mahmud and Mohammad Nurul Huda},
  doi       = {10.14569/IJACSA.2012.031115},
  url       = {https://doi.org/10.14569/IJACSA.2012.031115}
}