  This paper discusses the dominancy of local features (LFs), as input to the
multilayer neural network (MLN), extracted from a Bangla input speech over mel
frequency cepstral coefficients (MFCCs). Here, LF-based method comprises three
stages: (i) LF extraction from input speech, (ii) phoneme probabilities
extraction using MLN from LF and (iii) the hidden Markov model (HMM) based
classifier to obtain more accurate phoneme strings. In the experiments on
Bangla speech corpus prepared by us, it is observed that the LFbased automatic
speech recognition (ASR) system provides higher phoneme correct rate than the
MFCC-based system. Moreover, the proposed system requires fewer mixture
components in the HMMs.
