<abstract xmlns="http://www.w3.org/1999/xhtml"><p> 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).</p></abstract>

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).

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

Institute of Computer Science and Telecommunications, Reshetnev Siberian State Aerospace University, Krasnoyarsky rabochy Av. 31, 660037, Krasnoyarsk, Russian Federation

Institute of Communications Engineering, Ulm University, Albert Einstein-Allee 43, 89081, Ulm, Germany

Journal of Artificial Intelligence and Soft Computing Research

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

If conventional feature selection methods do not show sufficient effectiveness, alternative algorithmic schemes might be used. In this paper we propose an...

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

Data publikacji: 10 sie 2016

Zakres stron: 243 - 253

DOI: https://doi.org/10.1515/jaiscr-2016-0018

Słowa kluczowe
multi-objective optimization, feature selection, speech-based emotion recognition

© 2016

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

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

Data publikacji: 10 sie 2016

Zakres stron: 243 - 253

DOI: https://doi.org/10.1515/jaiscr-2016-0018

Słowa kluczowemulti-objective optimization, feature selection, speech-based emotion recognition

© 2016

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Słowa kluczowe
multi-objective optimization, feature selection, speech-based emotion recognition