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Features extraction for speech emotion

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 t...

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Main Authors: Kamaruddin, Norhaslinda, Abdul Rahman, Abdul Wahab
Format: Article
Language:English
Published: 2009
Subjects:
T Technology (General)
Online Access:http://irep.iium.edu.my/9565/1/Features_Extraction.pdf
http://irep.iium.edu.my/9565/
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spelling my.iium.irep.95652012-04-27T02:05:08Z http://irep.iium.edu.my/9565/ Features extraction for speech emotion Kamaruddin, Norhaslinda Abdul Rahman, Abdul Wahab T Technology (General) 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. 2009 Article REM application/pdf en http://irep.iium.edu.my/9565/1/Features_Extraction.pdf 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)
institution Universiti Islam Antarabangsa Malaysia
building IIUM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider International Islamic University Malaysia
content_source IIUM Repository (IREP)
url_provider http://irep.iium.edu.my/
language English
topic T Technology (General)
spellingShingle T Technology (General)
Kamaruddin, Norhaslinda
Abdul Rahman, Abdul Wahab
Features extraction for speech emotion
description 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.
format Article
author Kamaruddin, Norhaslinda
Abdul Rahman, Abdul Wahab
author_facet Kamaruddin, Norhaslinda
Abdul Rahman, Abdul Wahab
author_sort Kamaruddin, Norhaslinda
title Features extraction for speech emotion
title_short Features extraction for speech emotion
title_full Features extraction for speech emotion
title_fullStr Features extraction for speech emotion
title_full_unstemmed Features extraction for speech emotion
title_sort features extraction for speech emotion
publishDate 2009
url http://irep.iium.edu.my/9565/1/Features_Extraction.pdf
http://irep.iium.edu.my/9565/
_version_ 1643606281029681152
score 13.211869

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