Evolutionary Feature Selection for Emotion Recognition in Multilingual Speech Analysis


In the case when conventional feature selection methods do not demonstrate sufficient performance, alternative algorithmic schemes might be applied. In this paper we propose an evolutionary feature selection technique based on the two-criteria optimization model. To diminish the drawbacks of genetic algorithms, which are used as optimizers, we design a parallel multi-criteria heuristic procedure based on an island model. The effectiveness of the proposed approach was investigated on the Speech-based Emotion Recognition Problem, which reflects one of the crucial aspects in the sphere of human-machine communications. A number of multilingual corpora (German, English and Japanese) were engaged in the experiments. According to the results obtained, a high level of emotion recognition was achieved (up to a 11.15% relative improvement compared with the best F-score value on the full set of attributes).