Speech Emotion Recognition by using Philips Fingerprint and Spectral Entropy
Authors: 
Xuan Cao, Maoshen Jia, Lu Li, Tun-Wen PaiAuthors Info & Claims
Xuan Cao, Maoshen Jia, Lu Li, Tun-Wen PaiAuthors Info & ClaimsPages 214 - 220
Abstract
Speech Emotion Recognition (SER) has become an indispensable part of human-computer interaction. In order to obtain a higher recognition rate, in this paper, a joint feature based on Philips fingerprint and spectral entropy is used, and it is combined with some underlying features to perform speech emotion recognition on the four emotions of angry, neutral, happy, and sad. Firstly, the Mel Frequency Cepstral Coefficients (MFCC), Linear Prediction Coefficient (LPC), logarithmic amplitude-frequency characteristics, Philips fingerprints and spectral entropy are extracted for speech dataset. Secondly, Principal Component Analysis (PCA) is used to reduce the dimension of the extracted feature. Thirdly, SVM is used to train and classify the features after dimension reduction. The experimental results show that by comparing the recognition results with other feature combinations, the method proposed in this paper has good recognition results.
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- Speech Emotion Recognition by using Philips Fingerprint and Spectral Entropy
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March 2022
809 pages
ISBN:9781450396110
DOI:10.1145/3532213
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Published: 13 July 2022
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ICCAI '22
ICCAI '22: 2022 8th International Conference on Computing and Artificial Intelligence
March 18 - 21, 2022
Tianjin, China
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