Abstract
The current paper deals with the use of multivariate data analysis and decision tree methods in order to reduce the feature set for the normal and special children speech in four different emotions: anger, happiness, neutral and sadness. Ten features were extracted, by an algorithm implemented in a previous study to classify the speech emotions of normal and special children. In the current study, the best features are selected using multivariate analysis: principal component analysis (PCA), factor analysis and decision tree. Step by step PCA is applied to reduce the feature set according to the variables that are collinear. The obtained reduced feature sets are applicable to both normal and special children samples. Experimental results revealed that PCA yields the feature set comprising pitch, intensity, formant, LPCC and rate of acceleration. Factor analysis provides three feature sets out of which the feature set comprising of Rasta PLP, MFCC, ZCR, and intensity provides the best result. Decision tree yields a feature set comprising energy, pitch and LPCC.
Highlights
Emotion recognition system identifies the emotional state from voice [1], it is called speech emotion recognition (SER)
Seven features and three coding schemes are taken into consideration for analysis: pitch, intensity, formant, rate of acceleration, zero crossing rate (ZCR), log energy, log power, Mel frequency cepstrum coefficient (MFCC), linear prediction cepstrum coefficient (LPCC), Relative spectrum transforms perceptual linear prediction (Rasta PLP)
1) Experimental Results Based on principal component analysis (PCA) The variance accounted for by a given component is represented by the eigenvalue of each component
Summary
Emotion recognition system identifies the emotional state from voice [1], it is called speech emotion recognition (SER). There are four modules of SER: input, feature extraction, feature selection and classification of emotions [2]. Pitch, intensity and duration were used in early research studies. LPCC and MFCC were accompanied in the speech feature set [5]. In [6], 40 depressed patients and 40 control subjects were used in a study for speech feature analysis. Characteristics of depressed patients were found using ANOVA analysis and the results were linked to Gaussian mixture model (GMM) and support vector machine (SVM). Autism spectrum disorder comorbid for children (ASD-CC) psychometric properties were evaluated and developed in [7]. Confirmatory factor analysis (CFA) is used for the factor structure of the Korean version of ASD-CC
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