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    Bispectral features and mean shift clustering for stress and emotion recognition from natural speech
    (Pergamon-Elsevier Science Ltd, 2017) C. K., Yogesh; Muthusamy, Hariharan; Rajamanickam, Yuvaraj; Ngadiran, Ruzelita; Adom, A. H.; Polat, Kemal
    A new set of features and feature enhancement techniques are proposed to recognize emotion and stress from speech signal. The speech waveforms and the glottal waveforms (derived from the recorded emotional/stress speech waveforms) were processed by using third order statistics called bispectrum and 28 (14 from speech waveforms and 14 from glottal waveforms) bispectral based features. In this work, mean shift clustering was used to enhance the discrimination ability of the extracted Bispectral Features (BSFs). Four classifiers were used to distinguish different emotional and stressed states. The performance of the proposed method is tested with three databases. Different experiments were conducted and recognition rates were achieved in the range between 93.44% and 100% for Berlin emotional speech database (BES), between 73.81% and 97.23% for Surrey audio-visual expressed emotion database (SAVEE), between 93.8% and 100% for speech under simulated and actual stress simulated domain (SUSAS) (recognition of multi-style speech under stress-neutral, loud, lombard and anger) and 100% for SUSAS actual domain (recognition of three different levels of stress. high, medium and low). The obtained results indicate that the proposed bispectral based features and mean shift clustering provide promising results to recognize emotion and stress from speech signal. (C) 2017 Elsevier Ltd. All rights reserved.
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    Hybrid BBO_PSO and higher order spectral features for emotion and stress recognition from natural speech
    (Elsevier, 2017) Yogesh, C. K.; Hariharan, Muthusamy; Ngadiran, Ruzelita; Adom, A. H.; Yaacob, Sazali; Polat, Kemal
    The aim of the present study is to select a set of higher order spectral features for emotion/stress recognition system. 50 Bispectral (28 features) and Bicoherence (22 features) based higher order spectral features were extracted from speech signal and its glottal waveform. These features were combined with Inter-Speech 2010 features to further improve the recognition rates. Feature subset selection (FSS) was carried out in this proposed work with the objective of maximizing emotion recognition rate for subject independent with minimum features. The FSS contains two stages: Multi-cluster feature selection was adopted in Stage 1 to reduce feature space and identify relevant feature subset from Interspeech 2010 features. In Stage 2, Biogeography based optimization (BBO), Particle swarm optimization (PSO) and proposed BBO_PSO Hybrid optimization were performed to further reduce the dimension of feature space and identify the most relevant feature subset, which has higher discrimination ability to distinguish different emotional states. The proposed method was tested in three different databases: Berlin emotional speech database (BES), Surrey audio-visual expressed emotion database (SAVEE) and Speech under simulated and actual stress (SUSAS) simulated domain. The proposed feature set was evaluated with subject independent (SI), subject dependent (SD), gender dependent male (GD-male), gender dependent female (GD-female), text independent pairwise speech (TIDPS), and text independent multi-style speech (TIDMSS) experiments by using SVM and ELM classifiers. From the results obtained, it is evident that the proposed method attained accuracies of 93.25% (SI), 100% (SD), 93.75% (GD-male), and 97.58% (GD-female) for BES; 62.38% (SI) and 76.19% (SD) for SAVEE; and 90.09% (TIDMSS), 97.04% (TIDPS - Angryvs. Neutral), 98.89% (TIDPS - Lombard vs. Neutral), 99.07% (TIDPS - Loud vs. Neutral) for SUSAS. (c) 2017 Elsevier B.V. All rights reserved.