Abstract
This paper discusses a phonetic feature (PF) based automatic speech recognition system (ASR) for Bangla (widely known as Bengali), where the PF features are enhanced. There are three stages in this method where the first step maps Acoustic Features (AFs) or Local Features (LFs) into Phonetic Features (PFs) and the second step incorporates inhibition/enhancement (In/En) algorithm to change the PF dynamic patterns where patterns are enhanced for convex patterns and inhibited for concave patterns. The final step is for normalizing the extended PF vector using Gram-Schmidt algorithm and then passing through a Hidden Markov Model (HMM) based classifier. In our experiment on speech corpus for Bangla, the proposed feature extraction method provides higher sentence correct rate (SCR), word correct rate (WCR) and word accuracy (WA) compared to the methods that not incorporated In/En network.
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