Title: Integrated models and features-based speaker independent emotion recognition

Authors: C. Jeyalakshmi; A. Revathi; Y. Yenkataramani

Addresses: Department of Electronics and Communication Engineering, Trichy Engineering College, Trichy, Tamilnadu 621 132, India; Department of Electronics and Communication Engineering, Saranathan College of Engineering, Tamilnadu 620 012, India ' Department of Electronics and Communication Engineering, Trichy Engineering College, Trichy, Tamilnadu 621 132, India; Department of Electronics and Communication Engineering, Saranathan College of Engineering, Tamilnadu 620 012, India ' Department of Electronics and Communication Engineering, Saranathan College of Engineering, Tamilnadu 620 012, India

Abstract: Speech emotion recognition has become a challenging task in speech technology in order to ensure better and effective human-machine interaction. Improving the accuracy of speaker independent emotion recognition system by using a database containing the speech samples of limited number of utterances spoken by limited set of speakers is more challenging. This paper mainly discusses the use of integrated features and integrated models for improving the accuracy of the emotion recognition system. Integrated features are obtained by concatenating the probability with the normal system features. Integrated models are obtained by combining clustering and continuous density hidden Markov models. Integrated models with Mel frequency perceptual linear predictive cepstrum concatenated with probability provides better accuracy of 89% for recognising emotions using EMO-DB which contains speech samples of ten different utterances of ten different speakers in different emotions such as anger, boredom, disgust, fear, happy, neutral and sad by applying spectral analysis as a additional pre processing technique.

Keywords: emotion recognition; vector quantisation; VQ; Mel frequency perceptual linear predictive cepstrum; MFPLPC; probability; spectral analysis; short-time energy; zero crossing rate; continuous density HMM; hidden Markov model; CDHMM; speech recognition; human-machine interaction; HMI; clustering; anger; boredom; disgust; fear; happy; neutral; sadness.

DOI: 10.1504/IJTMCP.2016.077920

International Journal of Telemedicine and Clinical Practices, 2016 Vol.1 No.3, pp.277 - 291

Received: 02 Jul 2015
Accepted: 07 Sep 2015
Published online: 22 Jul 2016 *

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