Thursday 28th of March 2024
 

A Study of Speech Emotion and Speaker Identification System using VQ and GMM


Sushma Bahuguna and Y. P. Raiwani

This paper describes a text independent, closed set, speaker identification system to identify the speaker along with the emotional expression (Emo-voice Model) of the particular speech sentence. The system is evaluated on recorded sample sentences of native Hindi speakers in five basic emotions. Spectral Features, Mel Frequency cepstral coefficients have been used to implement emo-voice models using Vector Quantization and Gaussian Mixture modeling techniques for selected sample sentences using MATLAB. The VQ model trained with K-mean algorithm achieves as much as 82.7% of speaker identification with correct emotion accuracy whilst GMM model trained with EM algorithm achieves 87.9% of speaker identification with correct emotion accuracy. The statistical approach of Emo-voice Models could be used to extend the application field of voiceprint recognition technology.

Keywords: Emo-voice model, EM, GMM, K-mean, VQ

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ABOUT THE AUTHORS

Sushma Bahuguna
BCIIT (Affiliated to GGSIPU, Delhi)

Y. P. Raiwani
HNB Garhwal University, Srinagar, Uttarakhand, India


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