ArticleSensors (Basel, Switzerland)2020
Speech Emotion Recognition Based on Selective Interpolation Synthetic Minority Over-Sampling Technique in Small Sample Environment.
Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- [Research on bimodal emotion recognition algorithm based on multi-branch bidirectional multi-scale time perception].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2025Article
- Addressing data imbalance in collision risk prediction with active generative oversampling.Scientific reports · 2025Article
- In-Hospital Mortality Prediction Model for Critically Ill Older Adult Patients Transferred from the Emergency Department to the Intensive Care Unit.Risk management and healthcare policy · 2023Article
- Tool Wear Monitoring in Milling Based on Fine-Grained Image Classification of Machined Surface Images.Sensors (Basel, Switzerland) · 2022Article
- Predictors of Maternal Death Among Women With Pulmonary Hypertension in China From 2012 to 2020: A Retrospective Single-Center Study.Frontiers in cardiovascular medicine · 2022Article
- Bidirectional parallel echo state network for speech emotion recognition.Neural computing & applications · 2022Article
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5 authors.
Funding
Abstract
Speech emotion recognition often encounters the problems of data imbalance and redundant features in different application scenarios. Researchers usually design different recognition models for different sample conditions. In this study, a speech emotion recognition model for a small sample environment is proposed. A data imbalance processing method based on selective interpolation synthetic minority over-sampling technique (SISMOTE) is proposed to reduce the impact of sample imbalance on emotion recognition results. In addition, feature selection method based on variance analysis and gradient boosting decision tree (GBDT) is introduced, which can exclude the redundant features that possess poor emotional representation. Results of experiments of speech emotion recognition on three databases (i.e., CASIA, Emo-DB, SAVEE) show that our method obtains average recognition accuracy of 90.28% (CASIA), 75.00% (SAVEE) and 85.82% (Emo-DB) for speaker-dependent speech emotion recognition which is superior to some state-of-the-arts works.
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