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Phoneme Segmentation of Tamil Speech Signals Using Spectral Transition Measure


Affiliations
1 Department of Computer Science, D.J. Academy for Managerial Excellence, Coimbatore, 641 032, India
2 Department of Information Technology, Bharathiar University, India
 

Process of identifying the end points of the acoustic units of the speech signal is called speech segmentation.  Speech recognition systems can be designed using sub-word unit like phoneme. A Phoneme is the smallest unit of the language. It is context dependent and tedious to find the boundary.  Automated phoneme segmentation is carried in researches using Short term Energy, Convex hull, Formant, Spectral Transition Measure(STM), Group Delay Functions, Bayesian Information Criterion, etc.  In this research work, STM is used to find the phoneme boundary of Tamil speech utterances.  Tamil spoken word dataset was prepared with 30 words uttered by 4 native speakers with a high quality microphone. The performance of the segmentation is analysed and results are presented.

Keywords

Speech Recognition, Speech Segmentation, Spectral Transition Measure (STM), Phoneme Segmentation.
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  • Phoneme Segmentation of Tamil Speech Signals Using Spectral Transition Measure

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Authors

K. Geetha
Department of Computer Science, D.J. Academy for Managerial Excellence, Coimbatore, 641 032, India
R. Vadivel
Department of Information Technology, Bharathiar University, India

Abstract


Process of identifying the end points of the acoustic units of the speech signal is called speech segmentation.  Speech recognition systems can be designed using sub-word unit like phoneme. A Phoneme is the smallest unit of the language. It is context dependent and tedious to find the boundary.  Automated phoneme segmentation is carried in researches using Short term Energy, Convex hull, Formant, Spectral Transition Measure(STM), Group Delay Functions, Bayesian Information Criterion, etc.  In this research work, STM is used to find the phoneme boundary of Tamil speech utterances.  Tamil spoken word dataset was prepared with 30 words uttered by 4 native speakers with a high quality microphone. The performance of the segmentation is analysed and results are presented.

Keywords


Speech Recognition, Speech Segmentation, Spectral Transition Measure (STM), Phoneme Segmentation.

References





DOI: https://doi.org/10.13005/ojcst%2F10.01.15