Acoustic Emotion Recognition - Two Ways of Features Selection based on Self-Adaptive Multi-Objective Genetic Algorithm
Christina Brester, Maxim Sidorov, Eugene Stanislavovich Semenkin · 2014
In this paper the efficiency of feature selection techniques based on the evolutionary multi-objective optimization algorithm is investigated on the set of speech-based emotion recognition problems (English, German languages). Benefits of developed algorithmic schemes are demonstrated compared with Principal Component Analysis for the involved databases. Presented approaches allow not only to reduce the amount of features used by a classifier but also to improve its performance. According to the obtained results, the usage of proposed techniques might lead to increasing the emotion recognition accuracy by up to 29.37% relative improvement and reducing the number of features from 384 to 64.8 for some of the corpora.