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On the effect of feature compression on speech emotion recognition across multiple languages

Alghifari, Muhammad Fahreza and Gunawan, Teddy Surya and Nik Hashim, Nik Nur Wahidah and Wan Nordin, Mimi Aminah and Kartiwi, Mira (2020) On the effect of feature compression on speech emotion recognition across multiple languages. In: Springer's Lecture Nores in Electrical Engineering (LNEE). Springer. (In Press)

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Abstract

The ability of computers to recognize emotions from the speech is commonly termed as speech emotion recognition (SER). While in recent years, many studies have been performed, the golden standard has yet to be achieved due to many pa-rameters to consider. In this study, we investigate the effect of speech feature compression of Mel-frequency Cepstral Coefficient (MFCC) across four lan-guages – English, French, German, and Italian. The classification was performed using a deep feedforward network. The proposed methodology has shown to have significant results when tested using a network which was trained in the same language, and up to an accuracy rate of 80.8% when trained using all four languages.

Item Type: Book Chapter
Additional Information: 5588/84767
Uncontrolled Keywords: Speech Emotion Recognition, Average MFCC, neural network
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK7800 Electronics. Computer engineering. Computer hardware. Photoelectronic devices > TK7885 Computer engineering
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button): Kulliyyah of Engineering
Depositing User: Dr Teddy Surya Gunawan
Date Deposited: 23 Nov 2020 09:37
Last Modified: 23 Nov 2020 10:08
URI: http://irep.iium.edu.my/id/eprint/84767

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