Abstract
In the recent years, researchers are focusing to improve the accuracy of speech emotion recognition. Generally, high emotion recognition accuracies were obtained for two-class emotion recognition, but multi-class emotion recognition is still a challenging task . The main aim of this work is to propose a two-stage feature reduction using Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) for improving the accuracy of the speech emotion recognition (ER) system. Short-term speech features were extracted from the emotional speech signals. Experiments were carried out using four different supervised classifi ers with two different emotional speech databases. From the experimental results, it can be inferred that the proposed method provides better accuracies of 87.48% for speaker dependent (SD) and gender dependent (GD) ER experiment, 85.15% for speaker independent (SI) ER experiment, and 87.09% for gender independent (GI) experiment.
Highlights
To recognize human emotion, various modalities are used such as facial images and videos, speech and physiological signals
Short term cepstral features were extracted from the emotional speech signals of two different emotional speech databases
The performance of the speaker/gender independent emotion recognition (ER) is low compared to speaker/gender dependent ER
Summary
Various modalities are used such as facial images and videos, speech and physiological signals. Researchers have published several works on emotion recognition from spoken utterances (El Ayadi, Kamel, & Karray, 2011; Koolagudi & Rao, 2012). Spoken utterances of an individual can provide information about his/ her health state, emotion, language used and gender. Understanding of an individual’s emotion can be useful for applications like web movies, electronic tutoring applications, in-car board system, diagnostic tool for therapists and call-centre applications (El Ayadi et al, 2011; Koolagudi & Rao, 2012). Researchers have used four primary emotions such as happiness, sadness, anger, fear, surprise and disgust. The recognition accuracy between high-activation emotions and low-activation emotions are always high, but recognition between different emotions is still challenging (Wang & Guan, 2004)
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