Speech Emotion Recognition Based on Modified ReliefF
As the key of human-computer natural interaction, the research of emotion recognition is of great significance to the development of computer intelligence. In view of the issue that the current emotional feature dimension is too high, which affects the classification performance, this paper proposes a modified ReliefF feature selection algorithm to screen out feature subsets with smaller dimensions and better performance from high-dimensional features to further improve the efficiency and accuracy of emotion recognition. In the modified algorithm, the selection range of random samples is adjusted; the correlation between features is measured by the maximum information coefficient, and the distance measurement method between samples is established based on the correlation. The experimental results on the eNTERFACE'05 and SAVEE speech emotional datasets show that the features filtered based on the modified algorithm significantly reduce the data dimensions and effectively improve the accuracy of emotion recognition.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2022 |
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| Erschienen: |
2022 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:22 |
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| Enthalten in: |
Sensors (Basel, Switzerland) - 22(2022), 21 vom: 25. Okt. |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Li, Guo-Min [VerfasserIn] |
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| Links: |
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| Themen: |
Emotion recognition |
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| Anmerkungen: |
Date Completed 14.11.2022 Date Revised 17.11.2022 published: Electronic Citation Status MEDLINE |
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| doi: |
10.3390/s22218152 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM348794363 |
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| 500 | |a Citation Status MEDLINE | ||
| 520 | |a As the key of human-computer natural interaction, the research of emotion recognition is of great significance to the development of computer intelligence. In view of the issue that the current emotional feature dimension is too high, which affects the classification performance, this paper proposes a modified ReliefF feature selection algorithm to screen out feature subsets with smaller dimensions and better performance from high-dimensional features to further improve the efficiency and accuracy of emotion recognition. In the modified algorithm, the selection range of random samples is adjusted; the correlation between features is measured by the maximum information coefficient, and the distance measurement method between samples is established based on the correlation. The experimental results on the eNTERFACE'05 and SAVEE speech emotional datasets show that the features filtered based on the modified algorithm significantly reduce the data dimensions and effectively improve the accuracy of emotion recognition | ||
| 650 | 4 | |a Journal Article | |
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| 700 | 1 | |a Zhang, Jun-Ao |e verfasserin |4 aut | |
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