Title: Non-spectral features-based robust speaker independent emotion recognition from speech signal

Authors: A. Revathi; C. Jeyalakshmi; K. Thenmozhi

Addresses: Department of ECE/SEEE, SASTRA Deemed University, Thanjavur, India ' Department of ECE, K. Ramakrishnan College of Engineering, Samayapuram, Trichy, Tamilnadu, India ' Department of ECE/SEEE, SASTRA Deemed University, Thanjavur, India

Abstract: To ensure the better and effective human-machine interaction, affective computing is playing a vital role in the current scenario. Since speech and glottal signals convey the information about the emotional state of the speaker in addition to the linguistic data, it is essential to recognise the speaker's emotions and respond to it expressively. This paper mainly discusses the effectiveness of non-spectral features and modelling techniques to develop a robust multi-speaker independent speaker's emotion/stress recognition system. Since our EMO-DB database is small it has become a challenging task to improve the performance of the system. The proposed non-spectral features and modelling techniques provides 81% and 100% as weighted accuracy recall for the stress recognition system concerning the classification done on individual models and emotion-specific group models. Weighted accuracy recall is found to be 100% for the classification done on emotion-specific group models by considering the utterances from the SAVEE database.

Keywords: emotion recognition system; ERS; vector quantisation; VQ; energy; loudness; zero crossing rate; fundamental frequency; fuzzy C means clustering; FCM; minimum distance classifier.

DOI: 10.1504/IJMEI.2020.109944

International Journal of Medical Engineering and Informatics, 2020 Vol.12 No.5, pp.500 - 514

Received: 21 Apr 2018
Accepted: 18 Nov 2018
Published online: 30 Sep 2020 *

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