Improving Speech Emotion Recognition Using Frequency and Time Domain Acoustic Features
نویسندگان
چکیده
The recognition of the internal emotional state of a person plays an important role in several human-related fields. The present approach proposes the classification of 7 emotions (happiness, anger, fear, boredom, sadness, disgust and neutral) by using the speech signal. Different wavelet decomposition structures are used for feature vector extraction. The models were trained and tested with a Support Vectors Machine classifier.
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تاریخ انتشار 2011