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In this paper, we propose a global approach for speech emotion recognition (SER) system using empirical mode decomposition (EMD). Its use is motivated by the fact that the EMD combined with the Teager-Kaiser Energy Operator (TKEO) gives an efficient time-frequency analysis of the non-stationary signals. In this method, each signal is decomposed using EMD into oscillating components called intrinsic mode functions (IMFs). TKEO is used for estimating the time-varying amplitude envelope and instantaneous frequency of a signal that is supposed to be Amplitude Modulation-Frequency Modulation (AM-FM) signal. A subset of the IMFs was selected and used to extract features from speech signal to recognize different emotions. The main contribution of our work is to extract novel features named modulation spectral (MS) features and modulation frequency features (MFF) based on AM-FM modulation model and combined them with cepstral features. It is believed that the combination of all features will improve the performance of the emotion recognition system. Furthermore, we examine the effect of feature selection on SER system performance. For classification task, Support Vecto Machine (SVM) and Recurrent Neural Networks (RNN) are used to distinguish seven basic emotions. Two databases-the Berlin corpus, and the Spanish corpusare used for the experiments. The results evaluated on the Spanish emotional database, using RNN classifier and a combination of all features extracted from the IMFs enhances the performance of the SER system and achieving 91.16 % recognition rate. For the Berlin database, the combination of all features using SVM classifier has 86.22% recognition rate.

This chapter presents a comparative study of speech emotion recognition (SER) systems. Theoretical definition, categorization of affective state and the modalities of emotion expression are presented. To achieve this study, an SER system, based on different classifiers and different methods for features extraction, is developed. Mel-frequency cepstrum coefficients (MFCC) and modulation spectral (MS) features are extracted from the speech signals and used to train different classifiers. Feature selection (FS) was applied in order to seek for the most relevant feature subset. Several machine learning paradigms were used for the emotion classification task. A recurrent neural network (RNN) classifier is used first to classify seven emotions. Their performances are compared later to multivariate linear regression (MLR) and support vector machines (SVM) techniques, which are widely used in the field of emotion recognition for spoken audio signals. Berlin and Spanish databases are used as the experimental data set. This study shows that for Berlin database all classifiers achieve an accuracy of 83% when a speaker normalization (SN) and a feature selection are applied to the features. For Spanish database, the best accuracy (94 %) is achieved by RNN classifier without SN and with FS.

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Objective: To determine the prevalence of obesity and body fat distribution of Moroccan women of childbearing age, using a panel of anthropometric measurements. Design and setting: A cross-sectional survey conducted in 1995 in an agricultural community, El Jadida province of Morocco. Weight, height, waist and hip circumferences and triceps, biceps, subscapular and supra-iliac skinfold thicknesses were measured. Body mass index (BMI), waist/hip ratio (WHR), sum of all and sum of trunk skinfold thicknesses were determined. Subjects: In total, 1269 women aged 15 -49 years from urban and rural areas were surveyed. Results: The means of all anthropometric measurements including body fat were higher in urban than in rural women and increased with age. Trunk fat contributed 50% of total fat. Globally, 4.7% of women were underweight (BMI , 18.5 kg m The prevalence of overweight and obesity was higher in the urban than in the rural area. Underweight prevalence decreased with age, whereas that of overweight and obesity increased. All anthropometric parameters adjusted for age increased with the increase of BMI and WHR. Conclusions: Although undernutrition is still prevalent, there is an alarming prevalence of overweight and obesity in Moroccan women of childbearing age. The results indicate a shift in this country from the problem of dietary deficiency to the problem of dietary excess, and alert one to the necessity of establishing an intervention to prevent obesity-related diseases. It is necessary to address which of the anthropometric variables studied here is the best predictor of obesity-related diseases in this population.