Multi-Objective Heuristic Feature Selection for Speech-Based Multilingual Emotion Recognition

If conventional feature selection methods do not show sufficient effectiveness, alternative algorithmic schemes might be used. In this paper we propose an evolutionary feature selection technique based on the two-criterion optimization model. To diminish the drawbacks of genetic algorithms, which ar...

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Bibliographic Details
Published in:Journal of Artificial Intelligence and Soft Computing Research 2016-10, Vol.6 (4), p.243-253
Main Authors: Brester, Christina, Semenkin, Eugene, Sidorov, Maxim
Format: Article
Language:English
Subjects:
Design optimization
Emotion recognition
Emotions
Feature recognition
Feature selection
Genetic algorithms
Heuristic
Multilingualism
Multiple criterion
Multiple objective analysis
Speech recognition
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Summary:If conventional feature selection methods do not show sufficient effectiveness, alternative algorithmic schemes might be used. In this paper we propose an evolutionary feature selection technique based on the two-criterion optimization model. To diminish the drawbacks of genetic algorithms, which are applied as optimizers, we design a parallel multicriteria heuristic procedure based on an island model. The performance of the proposed approach was investigated on the Speech-based Emotion Recognition Problem, which reflects one of the most essential points in the sphere of human-machine communications. A number of multilingual corpora (German, English and Japanese) were involved in the experiments. According to the results obtained, a high level of emotion recognition was achieved (up to a 12.97% relative improvement compared with the best F-score value on the full set of attributes).
ISSN:2449-6499
DOI:10.1515/jaiscr-2016-0018