Speech Emotion recognition feature Extraction and Classification
نویسندگان
چکیده
منابع مشابه
Feature Extraction and Selection in Speech Emotion Recognition
Speech Emotion Recognition (SER) is a hot research topic in the field of Human Computer Interaction (HCI). In this paper, we recognize three emotional states: happy, sad and neutral. The explored features include: energy, pitch, linear predictive spectrum coding (LPCC), Mel-frequency spectrum coefficients (MFCC), and Mel-energy spectrum dynamic coefficients (MEDC). A German Corpus (Berlin Datab...
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One of the important issues in speech emotion recognizing is selecting of appropriate feature sets in order to improve the detection rate and classification accuracy. In last studies researchers tried to select the appropriate features for classification by using the selecting and reducing the space of features methods, such as the Fisher and PCA. In this research, a hybrid evolutionary algorit...
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Speech emotion is divided into four categories, Fear, Happy, Neutral and Surprise in this paper. Traditional features and their statistics are generally applied to recognize speech emotion. In order to quantify each feature’s contribution to emotion recognition, a method based on the Back Propagation (BP) neural network is adopted. Then we can obtain the optimal subset of the features. What’s m...
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ژورنال
عنوان ژورنال: International Journal of Advanced Trends in Computer Science and Engineering
سال: 2020
ISSN: 2278-3091
DOI: 10.30534/ijatcse/2020/54922020