Automatic Speaker Recognition System Using Fuzzy C-Means Artificial Neural Networks

Automatic Speaker Recognition System Using Fuzzy C-Means Artificial Neural Networks

Speaker recognition is a process of recognizing someone by their voice. The goal of speaker recognition is to extract, characterize and recognize the information about speaker identity. In this paper, we discussed both Fuzzy C-Means (FCM) and Artificial Neural Network (ANN) approach to speaker recog...

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Main Authors: M. Z., Ibrahim, Marzuki, Khalid, Rubiyah, Yusof
Format: Article
Language:English
English
Published: Universiti Malaysia Pahang 2008
Subjects:
TK Electrical engineering. Electronics Nuclear engineering
Online Access:http://umpir.ump.edu.my/id/eprint/8755/
http://umpir.ump.edu.my/id/eprint/8755/
http://umpir.ump.edu.my/id/eprint/8755/1/fkee-2008-zamri-%20Automatic%20Speaker%20Recognition%20System.pdf
http://umpir.ump.edu.my/id/eprint/8755/7/fkee-2008-zamri-%20Automatic%20Speaker%20Recognition%20System1.pdf
id ump-8755
recordtype eprints
spelling ump-87552016-09-05T03:04:53Z http://umpir.ump.edu.my/id/eprint/8755/ Automatic Speaker Recognition System Using Fuzzy C-Means Artificial Neural Networks M. Z., Ibrahim Marzuki, Khalid Rubiyah, Yusof TK Electrical engineering. Electronics Nuclear engineering Speaker recognition is a process of recognizing someone by their voice. The goal of speaker recognition is to extract, characterize and recognize the information about speaker identity. In this paper, we discussed both Fuzzy C-Means (FCM) and Artificial Neural Network (ANN) approach to speaker recognition system. The proposed system comprises of three main modules, a feature extraction module to extract necessary features from speech waves, speaker modeling module to generate the speaker model and FCM and ANN module to classify the speakers whether to accept or reject. The proposed intelligent learning system has been applied to a case study of text-dependent speaker recognition system and the performance is evaluated by applying two types of feature extraction techniques: Mel Frequency Cepstral Coefficients (MFCC) and Linear Predictive Ceps~ral Coefficients (LPCC). Experiment showed that the new proposed systems provide significantly higher performance compare to conventional method. Universiti Malaysia Pahang 2008 Article PeerReviewed application/pdf en http://umpir.ump.edu.my/id/eprint/8755/1/fkee-2008-zamri-%20Automatic%20Speaker%20Recognition%20System.pdf application/pdf en http://umpir.ump.edu.my/id/eprint/8755/7/fkee-2008-zamri-%20Automatic%20Speaker%20Recognition%20System1.pdf M. Z., Ibrahim and Marzuki, Khalid and Rubiyah, Yusof (2008) Automatic Speaker Recognition System Using Fuzzy C-Means Artificial Neural Networks. Jurnal UMP Kejuruteraan & Teknologi Komputer, 1 (1). pp. 93-108. ISSN 1985-5176 http://www.ump.edu.my/
repository_type Digital Repository
institution_category Local University
institution Universiti Malaysia Pahang
building UMP Institutional Repository
collection Online Access
language English
English
topic TK Electrical engineering. Electronics Nuclear engineering
spellingShingle TK Electrical engineering. Electronics Nuclear engineering
M. Z., Ibrahim
Marzuki, Khalid
Rubiyah, Yusof
Automatic Speaker Recognition System Using Fuzzy C-Means Artificial Neural Networks
description Speaker recognition is a process of recognizing someone by their voice. The goal of speaker recognition is to extract, characterize and recognize the information about speaker identity. In this paper, we discussed both Fuzzy C-Means (FCM) and Artificial Neural Network (ANN) approach to speaker recognition system. The proposed system comprises of three main modules, a feature extraction module to extract necessary features from speech waves, speaker modeling module to generate the speaker model and FCM and ANN module to classify the speakers whether to accept or reject. The proposed intelligent learning system has been applied to a case study of text-dependent speaker recognition system and the performance is evaluated by applying two types of feature extraction techniques: Mel Frequency Cepstral Coefficients (MFCC) and Linear Predictive Ceps~ral Coefficients (LPCC). Experiment showed that the new proposed systems provide significantly higher performance compare to conventional method.
format Article
author M. Z., Ibrahim
Marzuki, Khalid
Rubiyah, Yusof
author_facet M. Z., Ibrahim
Marzuki, Khalid
Rubiyah, Yusof
author_sort M. Z., Ibrahim
title Automatic Speaker Recognition System Using Fuzzy C-Means Artificial Neural Networks
title_short Automatic Speaker Recognition System Using Fuzzy C-Means Artificial Neural Networks
title_full Automatic Speaker Recognition System Using Fuzzy C-Means Artificial Neural Networks
title_fullStr Automatic Speaker Recognition System Using Fuzzy C-Means Artificial Neural Networks
title_full_unstemmed Automatic Speaker Recognition System Using Fuzzy C-Means Artificial Neural Networks
title_sort automatic speaker recognition system using fuzzy c-means artificial neural networks
publisher Universiti Malaysia Pahang
publishDate 2008
url http://umpir.ump.edu.my/id/eprint/8755/
http://umpir.ump.edu.my/id/eprint/8755/
http://umpir.ump.edu.my/id/eprint/8755/1/fkee-2008-zamri-%20Automatic%20Speaker%20Recognition%20System.pdf
http://umpir.ump.edu.my/id/eprint/8755/7/fkee-2008-zamri-%20Automatic%20Speaker%20Recognition%20System1.pdf
first_indexed 2023-09-18T22:06:40Z
last_indexed 2023-09-18T22:06:40Z
_version_ 1777414753975533568

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