Comparing Multiple Classifiers for Speech-Based Detection of Self-Confidence - A Pilot Study

The aim of this study is to compare several classifiers commonly used within the field of speech emotion recognition (SER) on the speech based detection of self-confidence. A standard acoustic feature set was computed, resulting in 170 features per one-minute speech sample (e.g. fundamental frequency, intensity, formants, MFCCs). In order to identify speech correlates of self-confidence, the lectures of 14 female participants were recorded, resulting in 306 one-minute segments of speech. Five expert raters independently assessed the self-confidence impression. Several classification models (e.g. Random Forest, Support Vector Machine, Naïve Bayes, Multi-Layer Perceptron) and ensemble classifiers (AdaBoost, Bagging, Stacking) were trained. AdaBoost procedures turned out to achieve best performance, both for single models (AdaBoost LR: 75.2% class-wise averaged recognition rate) and for average boosting (59.3%) within speaker-independent settings.