Improving recognition of syallabic units of Hindi languagae using combined features of Throat Microphone and Normal Microphone speech

Abstract

The performance of Automatic Speech recognition system (ASR) built using close talk microphones degrades in noisy environments. AS R built using Throat Microphone (TM) speech shows relatively better performance under such adverse situations. However, some of the sounds are not well captured in TM. In this work we explore the combined use of Normal Microphone (NM) and TM features to improve the recognition rate of AS R. In the proposed work, the combined Mel-Frequency Cepstral Coefficients (MFCC) derived from the two signals are used to built an AS R in the HMM framework to recognize the 145 syllabic units of Indian language Hindi. The performance of this combined AS R system shows a significant improvement in performance when compared with individual AS R systems built using NM and TM features, respectively.