An Experimental Analysis of Speech Features for Tone Speech Recognition

Recently Automatic Speech Recognition (ASR) has been successfully integrated in many commercial applications. These applications are performing significantly well in relatively controlled acoustical environments. However, the performance of an Automatic Speech Recognition system developed for non-to...

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Bibliographic Details
Published inInternational journal of innovative technology and exploring engineering Vol. 2; no. 9; pp. 4355 - 4360
Main Authors Bhattacharjee, Utpal, Mannala, Jyoti
Format Journal Article
LanguageEnglish
Published 30.12.2019
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Summary:Recently Automatic Speech Recognition (ASR) has been successfully integrated in many commercial applications. These applications are performing significantly well in relatively controlled acoustical environments. However, the performance of an Automatic Speech Recognition system developed for non-tonal languages degrades considerably when tested for tonal languages. One of the main reason for this performance degradation is the non-consideration of tone related information in the feature set of the ASR systems developed for non-tonal languages. In this paper we have investigated the performance of commonly used feature for tonal speech recognition. A model has been proposed for extracting features for tonal speech recognition. A statistical analysis has been done to evaluate the performance of proposed feature set with reference to the Apatani language of Arunachal Pradesh of North-East India, which is a tonal language of Tibeto-Burman group of languages.
ISSN:2278-3075
2278-3075
DOI:10.35940/ijitee.B7748.129219