An Experimental Analysis of Speech Features for Tone Speech Recognition
Utpal Bhattacharjee1, Jyoti Mannala2
1Utpal Bhattacharjee*, Department of Computer Science and Engineering, Rajiv Gandhi University, Rono Hills, Doimukh, Arunachal Pradesh, India.
2Jyoti Mannala, Department of Computer Science and Engineering, Rajiv Gandhi University, Rono Hills, Doimukh, Arunachal Pradesh, India.
Manuscript received on November 18, 2019. | Revised Manuscript received on 22 November, 2019. | Manuscript published on December 10, 2019. | PP: 4355-4360 | Volume-9 Issue-2, December 2019. | Retrieval Number: B7748129219/2019©BEIESP | DOI: 10.35940/ijitee.B7748.129219
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: 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.
Keywords: Keywords: Feature Selection, LPCC, MFCC, Tonal Language, Prosodic Features, Speech Recognition
Scope of the Article: Pattern Recognition