Improved Emotion Recognition Using Gaussian Mixture Model and Extreme Learning Machine in Speech and Glottal Signals

M. Hariharan · Kemal Polat · Sazali Yaacob

53 citations0/0 files storedMathematical Problems in Engineering

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Emotion and Mood Recognition Speech Recognition and Synthesis Machine Learning and ELM

Abstract

Recently, researchers have paid escalating attention to studying the emotional state of an individual from his/her speech signals as the speech signal is the fastest and the most natural method of communication between individuals. In this work, new feature enhancement using Gaussian mixture model (GMM) was proposed to enhance the discriminatory power of the features extracted from speech and glottal signals. Three different emotional speech databases were utilized to gauge the proposed methods. Extreme learning machine (ELM) and k -nearest neighbor ( k NN) classifier were employed to classify the different types of emotions. Several experiments were conducted and results show that the proposed methods significantly improved the speech emotion recognition performance compared to research works published in the literature.

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References

2001Emotion recognition in human-computer interaction 2005A database of German emotional speech 2010Survey on speech emotion recognition: Features, classification schemes, and databases 2006Emotional speech recognition: Resources, features, and methods 2012Emotion recognition from speech: a review 2010Automatic speech emotion recognition using modulation spectral features 2007Critical Analysis of the Impact of Glottal Features in the Classification of Clinical Depression in Speech 2008Acoustic feature selection for automatic emotion recognition from speech

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2018Brain signal based human emotion analysis by circular back propagation and Deep Kohonen Neural Networks 2017Hybrid BBO_PSO and higher order spectral features for emotion and stress recognition from natural speech

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