首页|Studies from Informatics Institute for Postgraduate Studies Provide New Data on Support Vector Machines (Using Speech Signal for Emotion Recognition Using Hybrid Features with SVM Classifier)
Studies from Informatics Institute for Postgraduate Studies Provide New Data on Support Vector Machines (Using Speech Signal for Emotion Recognition Using Hybrid Features with SVM Classifier)
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Research findings on support vector machines are discussed in a new report. According to news originating from the Informatics Institute for Postgraduate Studies by NewsRx correspondents, research stated, "Emotion recognition is a hot topic that has received a lot of attention and study,owing to its significance in a variety of fields, including applications needing human-computer interaction (HCI)." Our news reporters obtained a quote from the research from Informatics Institute for Postgraduate Studies: "Extracting features related to the emotional state of speech remains one of the important research challenges. This study investigated the approach of the core idea behind feature extraction is the residual signal of the prediction procedure is the difference between the original and the prediction .hence the visibility of using sets of extracting features from speech single when the statistical of local features were used to achieve high detection accuracy for seven emotions. The proposed approach is based on the fact that local features can provide efficient representations suitable for pattern recognition. Publicly available speech datasets like the Berlin dataset are tested using a support vector machine (SVM) classifier. The hybrid features were trained separately. The results indicated that some features were terrible."
Informatics Institute for Postgraduate StudiesMachine LearningSupport Vector Machines
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