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Features extraction for speech emotion

Kamaruddin, Norhaslinda and Abdul Rahman, Abdul Wahab (2009) Features extraction for speech emotion. Journal of Computational Methods in Science and Engineering , 9 (1). S1-S12. ISSN 14727978 (Print), 18758983 (Online)

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Abstract

In this paper the speech emotion verification using two most popular methods in speech processing and analysis based on the Mel-Frequency Cepstral Coefficient (MFCC) and the Gaussian Mixture Model (GMM) were proposed and analyzed. In both cases, features for the speech emotion were extracted using the Short Time Fourier Transform (STFT) and Short Time Histogram (STH) for MFCC and GMM respectively. The performance of the speech emotion verification is measured based on three neural network (NN) and fuzzy neural network (FNN) architectures; namely: Multi Layer Perceptron (MLP), Adaptive Neuro Fuzzy Inference System (ANFIS) and Generic Self-organizing Fuzzy Neural Network (GenSoFNN). Results obtained from the experiments using real audio clips from movies and television sitcoms show the potential of using the proposed features extraction methods for real time application due to its reasonable accuracy and fast training time. This may lead us to the practical usage if the emotion verifier can be embedded in real time applications especially for personal digital assistance (PDA) or smart-phones.

Item Type: Article (Journal)
Additional Information: 6145/9565
Subjects: T Technology > T Technology (General)
Kulliyyahs/Centres/Divisions/Institutes: Kulliyyah of Information and Communication Technology
Kulliyyah of Information and Communication Technology
Depositing User: Sis Zaleha Ibat
Date Deposited: 27 Apr 2012 10:04
Last Modified: 27 Apr 2012 10:05
URI: http://irep.iium.edu.my/id/eprint/9565

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