{"title":"A deep neuro-fuzzy framework for speech emotion recognition.","authors":"Qingqing Zhang","doi":"10.1080/10255842.2025.2559060","DOIUrl":null,"url":null,"abstract":"<p><p>Speech Emotion Recognition (SER) is crucial in fields like healthcare and education, requiring robust techniques for accurate emotion detection. This paper proposes a deep neuro-fuzzy framework combining Deep Neural Networks (DNN) and Adaptive Neuro-Fuzzy Inference Systems (ANFIS). It includes fuzzification, deep feature extraction, and defuzzification units, enhancing SER accuracy while addressing ANFIS limitations with high-dimensional data and DNN's lack of interpretability. The scheme's productivity on three standard speech databases is appraised: RML, SAVEE, and RAVDESS. The results indicate that our framework outperforms ANFIS, DNN, and pre-trained models, achieving up to 97.95% accuracy and showing strong potential for future SER research.</p>","PeriodicalId":50640,"journal":{"name":"Computer Methods in Biomechanics and Biomedical Engineering","volume":" ","pages":"1-12"},"PeriodicalIF":1.6000,"publicationDate":"2025-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computer Methods in Biomechanics and Biomedical Engineering","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1080/10255842.2025.2559060","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
引用次数: 0
Abstract
Speech Emotion Recognition (SER) is crucial in fields like healthcare and education, requiring robust techniques for accurate emotion detection. This paper proposes a deep neuro-fuzzy framework combining Deep Neural Networks (DNN) and Adaptive Neuro-Fuzzy Inference Systems (ANFIS). It includes fuzzification, deep feature extraction, and defuzzification units, enhancing SER accuracy while addressing ANFIS limitations with high-dimensional data and DNN's lack of interpretability. The scheme's productivity on three standard speech databases is appraised: RML, SAVEE, and RAVDESS. The results indicate that our framework outperforms ANFIS, DNN, and pre-trained models, achieving up to 97.95% accuracy and showing strong potential for future SER research.
期刊介绍：
The primary aims of Computer Methods in Biomechanics and Biomedical Engineering are to provide a means of communicating the advances being made in the areas of biomechanics and biomedical engineering and to stimulate interest in the continually emerging computer based technologies which are being applied in these multidisciplinary subjects. Computer Methods in Biomechanics and Biomedical Engineering will also provide a focus for the importance of integrating the disciplines of engineering with medical technology and clinical expertise. Such integration will have a major impact on health care in the future.
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