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This paper discusses the dominancy of local features (LFs), as input to themultilayer neural network (MLN), extracted from a Bangla input speech over melfrequency cepstral coefficients (MFCCs). Here, LF-based method comprises threestages: (i) LF extraction from input speech, (ii) phoneme probabilitiesextraction using MLN from LF and (iii) the hidden Markov model (HMM) basedclassifier to obtain more accurate phoneme strings. In the experiments onBangla speech corpus prepared by us, it is observed that the LFbased automaticspeech recognition (ASR) system provides higher phoneme correct rate than theMFCC-based system. Moreover, the proposed system requires fewer mixturecomponents in the HMMs.

Local Feature or Mel Frequency Cepstral Coefficients - Which One is Better for MLN-Based Bangla Speech Recognition?