On the comparison of line spectral frequencies and mel-frequency cepstral coefficients using feedforward neural network for language identification

Of the many audio features available, this paper focuses on the comparison of two most popular features, i.e. line spectral frequencies (LSF) and Mel-Frequency Cepstral Coefficients. We trained a feedforward neural network with various hidden layers and number of hidden nodes to identify five differ...

Full description

Bibliographic Details
Main Authors:	Gunawan, Teddy Surya, Kartiwi, Mira
Format:	Article
Language:	English English
Published:	Indonesian Journal of Electrical Engineering and Computer Science ( IAES) 2018
Subjects:	TK7885 Computer engineering
Online Access:	http://irep.iium.edu.my/61795/ http://irep.iium.edu.my/61795/ http://irep.iium.edu.my/61795/ http://irep.iium.edu.my/61795/1/10880-15119-1-PBGunawanLanguage.pdf http://irep.iium.edu.my/61795/7/61795_On%20the%20Comparison%20of%20Line%20Spectral%20Frequencies%20_scopus.pdf

id	iium-61795
recordtype	eprints
spelling	iium-617952018-02-13T03:05:26Z http://irep.iium.edu.my/61795/ On the comparison of line spectral frequencies and mel-frequency cepstral coefficients using feedforward neural network for language identification Gunawan, Teddy Surya Kartiwi, Mira TK7885 Computer engineering Of the many audio features available, this paper focuses on the comparison of two most popular features, i.e. line spectral frequencies (LSF) and Mel-Frequency Cepstral Coefficients. We trained a feedforward neural network with various hidden layers and number of hidden nodes to identify five different languages, i.e. Arabic, Chinese, English, Korean, and Malay. LSF, MFCC, and combination of both features were extracted as the feature vectors. Systematic experiments have been conducted to find the optimum parameters, i.e. sampling frequency, frame size, model order, and structure of neural network. The recognition rate per frame was converted to recognition rate per audio file using majority voting. On average, the recognition rate for LSF, MFCC, and combination of both features are 96%, 92%, and 96%, respectively. Therefore, LSF is the most suitable features to be utilized for language identification using feedforward neural network classifier. Indonesian Journal of Electrical Engineering and Computer Science ( IAES) 2018-04 Article PeerReviewed application/pdf en http://irep.iium.edu.my/61795/1/10880-15119-1-PBGunawanLanguage.pdf application/pdf en http://irep.iium.edu.my/61795/7/61795_On%20the%20Comparison%20of%20Line%20Spectral%20Frequencies%20_scopus.pdf Gunawan, Teddy Surya and Kartiwi, Mira (2018) On the comparison of line spectral frequencies and mel-frequency cepstral coefficients using feedforward neural network for language identification. Indonesian Journal of Electrical Engineering and Computer Science, 10 (1). pp. 168-175. ISSN 2502-4752 E-ISSN 2502-4760 http://iaescore.com/journals/index.php/IJEECS/ 10.11591/ijeecs.v10.i1.pp168-175
repository_type	Digital Repository
institution_category	Local University
institution	International Islamic University Malaysia
building	IIUM Repository
collection	Online Access
language	English English
topic	TK7885 Computer engineering
spellingShingle	TK7885 Computer engineering Gunawan, Teddy Surya Kartiwi, Mira On the comparison of line spectral frequencies and mel-frequency cepstral coefficients using feedforward neural network for language identification
description	Of the many audio features available, this paper focuses on the comparison of two most popular features, i.e. line spectral frequencies (LSF) and Mel-Frequency Cepstral Coefficients. We trained a feedforward neural network with various hidden layers and number of hidden nodes to identify five different languages, i.e. Arabic, Chinese, English, Korean, and Malay. LSF, MFCC, and combination of both features were extracted as the feature vectors. Systematic experiments have been conducted to find the optimum parameters, i.e. sampling frequency, frame size, model order, and structure of neural network. The recognition rate per frame was converted to recognition rate per audio file using majority voting. On average, the recognition rate for LSF, MFCC, and combination of both features are 96%, 92%, and 96%, respectively. Therefore, LSF is the most suitable features to be utilized for language identification using feedforward neural network classifier.
format	Article
author	Gunawan, Teddy Surya Kartiwi, Mira
author_facet	Gunawan, Teddy Surya Kartiwi, Mira
author_sort	Gunawan, Teddy Surya
title	On the comparison of line spectral frequencies and mel-frequency cepstral coefficients using feedforward neural network for language identification
title_short	On the comparison of line spectral frequencies and mel-frequency cepstral coefficients using feedforward neural network for language identification
title_full	On the comparison of line spectral frequencies and mel-frequency cepstral coefficients using feedforward neural network for language identification
title_fullStr	On the comparison of line spectral frequencies and mel-frequency cepstral coefficients using feedforward neural network for language identification
title_full_unstemmed	On the comparison of line spectral frequencies and mel-frequency cepstral coefficients using feedforward neural network for language identification
title_sort	on the comparison of line spectral frequencies and mel-frequency cepstral coefficients using feedforward neural network for language identification
publisher	Indonesian Journal of Electrical Engineering and Computer Science ( IAES)
publishDate	2018
url	http://irep.iium.edu.my/61795/ http://irep.iium.edu.my/61795/ http://irep.iium.edu.my/61795/ http://irep.iium.edu.my/61795/1/10880-15119-1-PBGunawanLanguage.pdf http://irep.iium.edu.my/61795/7/61795_On%20the%20Comparison%20of%20Line%20Spectral%20Frequencies%20_scopus.pdf
first_indexed	2023-09-18T21:27:38Z
last_indexed	2023-09-18T21:27:38Z
_version_	1777412298284990464

On the comparison of line spectral frequencies and mel-frequency cepstral coefficients using feedforward neural network for language identification

Similar Items