A Deep Learning Method Using Gender-Specific Features for Emotion Recognition

Li-Min Zhang and Yang Li and Yue-Ting Zhang and Giap Weng Ng and Yu-Beng Leau and Hao Yan (2023) A Deep Learning Method Using Gender-Specific Features for Emotion Recognition. Sensors, 23. pp. 1-15.

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Abstract

Speech reflects people’s mental state and using a microphone sensor is a potential method for human–computer interaction. Speech recognition using this sensor is conducive to the diagnosis of mental illnesses. The gender difference of speakers affects the process of speech emotion recognition based on specific acoustic features, resulting in the decline of emotion recognition accuracy. Therefore, we believe that the accuracy of speech emotion recognition can be effectively improved by selecting different features of speech for emotion recognition based on the speech representations of different genders. In this paper, we propose a speech emotion recognition method based on gender classification. First, we use MLP to classify the original speech by gender. Second, based on the different acoustic features of male and female speech, we analyze the influence weights of multiple speech emotion features in male and female speech, and establish the optimal feature sets for male and female emotion recognition, respectively. Finally, we train and test CNN and BiLSTM, respectively, by using the male and the female speech emotion feature sets. The results show that the proposed emotion recognition models have an advantage in terms of average recognition accuracy compared with gender-mixed recognition models

Item Type:	Article
Keyword:	Speech emotion recognition ; Gender classification ; CNN ; BiLSTM
Subjects:	P Language and Literature > PN Literature (General) > PN1-6790 Literature (General) > PN4699-5650 Journalism. The periodical press, etc. > PN4775-4784 Technique. Practical journalism Q Science > QA Mathematics > QA1-939 Mathematics > QA71-90 Instruments and machines > QA75.5-76.95 Electronic computers. Computer science
Department:	FACULTY > Faculty of Computing and Informatics
Depositing User:	ABDULLAH BIN SABUDIN -
Date Deposited:	21 Jul 2023 14:51
Last Modified:	21 Jul 2023 14:51
URI:	https://eprints.ums.edu.my/id/eprint/36091

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