Optimal feature selection for speech emotion recognition using enhanced cat swarm optimization algorithm
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Optimal feature selection for speech emotion recognition using enhanced cat swarm optimization algorithm M. Gomathy1 Received: 8 November 2019 / Accepted: 7 November 2020 © Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract Human interactions involve emotional cues that can be used to interpret the emotion expressed by the speaker. As the vocal emotions vary from one speaker to another, there is a chance of misinterpretation. To determine the emotion expressed by the speaker, a speech emotion recognizer can be utilized. It is known that speech expresses the emotional states of humans along with the syntax and semantic content of linguistic sentences. Therefore, human emotion recognition using speech signaling is possible. Speech emotion recognition is a crucial and challenging task in which the feature extraction plays a prominent role in its performance. Determining emotional states in speech signals is a very challenging area for many reasons. The first issue of all speech emotion systems is the selection of the best features, which is powerful enough to distinguish various emotions. The presence of different language, pronunciation, sentences, style, and speakers adds additional difficulty since these characteristics include pitch and energy that directly alters most of the features extracted. Redundant features and high computational cost make emotion recognition an undesirable task. Instead of focusing on the words, the vocal changes and communicative pressure on the words should be taken as the primary consideration. The Enhanced Cat Swarm Optimization (ECSO) algorithm for feature extraction has been proposed to address these issues and it is not used in any existing speech emotion recognition approaches. The proposed approach achieves excellent performance in terms of accuracy, recognition rate, sensitivity, and specificity. Keywords Speech emotion recognition · Cat swarm optimization · Opposition based learning · Support vector neural network · Feature extraction
1 Introduction The speech signal consists of linguistic information and also paralinguistic one such as emotion. The modern automatic speech recognition systems have achieved high performance in neutral style speech recognition (Gharavian et al. 2012). The acoustic and prosodic features of speech are affected by emotions and speaking styles as well as speaker characteristics and linguistic features. Although the emotional state does not alter the linguistic content, it is an important factor in human communication and improving the voicebased man–machine interactions (El Ayadi et al. 2011). Man–machine interaction is one of the key goals in developing automatic emotion recognition (AER) systems. The * M. Gomathy [email protected] 1
Department of Computer Science, Shrimati Indira Gandhi College, Tiruchirappalli 620002, India
AER system is a key component in many applications such as spoken tutoring systems, medical-emergency domain to detect stress and pain, interactions with robots, computer games, call centers, a
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