A novel speech emotion recognition method based on feature construction and ensemble learning
In the field of Human-Computer Interaction (HCI), speech emotion recognition technology plays an important role. Facing a small number of speech emotion data, a novel speech emotion recognition method based on feature construction and ensemble learning is proposed in this paper. Firstly, the acoustic features are extracted from the speech signal and combined to form different original feature sets. Secondly, based on Light Gradient Boosting Machine (LightGBM) and Sequential Forward Selection (SFS) method, a novel feature selection method named L-SFS is proposed. And then, the softmax regression model is used to learn automatically the weights of the four single weak learners including Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Extreme Gradient Boosting (XGBoost) and LightGBM. Lastly, based on the learned automatically weights and the weighted average probability voting strategy, an ensemble classification model named Sklex is constructed, which integrates the above four single weak learners. In conclusion, the method reflects the effectiveness of feature construction and the superiority and stability of ensemble learning, and gets good speech emotion recognition accuracy.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2022 |
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| Erschienen: |
2022 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:17 |
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| Enthalten in: |
PloS one - 17(2022), 8 vom: 16., Seite e0267132 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Guo, Yi [VerfasserIn] |
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| Links: |
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| Anmerkungen: |
Date Completed 17.08.2022 Date Revised 22.08.2022 published: Electronic-eCollection Citation Status MEDLINE |
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| doi: |
10.1371/journal.pone.0267132 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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NLM344882977 |
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| 245 | 1 | 2 | |a A novel speech emotion recognition method based on feature construction and ensemble learning |
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| 500 | |a Citation Status MEDLINE | ||
| 520 | |a In the field of Human-Computer Interaction (HCI), speech emotion recognition technology plays an important role. Facing a small number of speech emotion data, a novel speech emotion recognition method based on feature construction and ensemble learning is proposed in this paper. Firstly, the acoustic features are extracted from the speech signal and combined to form different original feature sets. Secondly, based on Light Gradient Boosting Machine (LightGBM) and Sequential Forward Selection (SFS) method, a novel feature selection method named L-SFS is proposed. And then, the softmax regression model is used to learn automatically the weights of the four single weak learners including Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Extreme Gradient Boosting (XGBoost) and LightGBM. Lastly, based on the learned automatically weights and the weighted average probability voting strategy, an ensemble classification model named Sklex is constructed, which integrates the above four single weak learners. In conclusion, the method reflects the effectiveness of feature construction and the superiority and stability of ensemble learning, and gets good speech emotion recognition accuracy | ||
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| 700 | 1 | |a Liu, Yangcheng |e verfasserin |4 aut | |
| 700 | 1 | |a Xu, Liang |e verfasserin |4 aut | |
| 700 | 1 | |a Li, Qiong |e verfasserin |4 aut | |
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