This paper presents a Bangla phoneme recognition method for Automatic Speech Recognition (ASR). The method consists of two stages: i) a multilayer neural network (MLN), which converts acoustic features, mel frequency cepstral coefficients (MFCCs), into phoneme probabilities and ii) the phoneme probabilities obtained from the first stage and corresponding Δ and ΔΔ parameters calculated by linear regression (LR) are inserted into a hidden Markov model (HMM) based classifier to obtain more accurate phoneme strings. From the experiments on Bangla speech corpus prepared by us, it is observed that the proposed method provides higher phoneme recognition performance than the existing method. Moreover, it requires a fewer mixture components in the HMMs.
Financed by the National Centre for Research and Development under grant No. SP/I/1/77065/10 by the strategic scientific research and experimental development program:
SYNAT - “Interdisciplinary System for Interactive Scientific and Scientific-Technical Information”.