Vowel classification based on frequency response of vocal tract

Link to publisher's homepage at http://ieeexplore.ieee.org

Saved in:
Bibliographic Details
Main Authors: Paulraj, M.P., Sazali, Yaacob, Shahrul Azmi, M. Y.
Format: Article
Language:English
Published: Institute of Electrical and Electronics Engineering (IEEE) 2009
Subjects:
Online Access:http://dspace.unimap.edu.my/xmlui/handle/123456789/6870
Tags: Add Tag
No Tags, Be the first to tag this record!
Institution: Universiti Malaysia Perlis
Language: English
id my.unimap-6870
record_format dspace
spelling my.unimap-68702010-11-23T06:36:46Z Vowel classification based on frequency response of vocal tract Paulraj, M.P. Sazali, Yaacob Shahrul Azmi, M. Y. Automatic speech recognition Speech recognition Signal classification Regression analysis Speech perception Natural language processing Link to publisher's homepage at http://ieeexplore.ieee.org Automatic speech recognition (ASR) has made great strides with the development of digital signal processing hardware and software especially using English as the language of choice. In this paper, a modified feature extraction approach based on frequency response model of the vocal tract and Bark Scale using vowel utterances from Malaysian speakers is presented. This technique calculates mean and maximum energy values from fixed frequency bands between 20Hz to 2500Hz. The frequency band sizes are 100Hz, 200Hz, 300Hz, 400Hz and 500Hz These results are then compared with mean and maximum values of first 14 critical bands of the bark scale. The energy features obtained are classified using Multinomial Logistic Regression and used to detect five vowels of /a/, /e/, /i/, /o/ and /u/ recorded from 80 Malaysian speakers. The classification results obtained from the 100Hz and 200Hz bands gave better result than the Bark Scale. Vowel /a/, /e/ and /i/ obtained a perfect 100% detection rate for both 100Hz and 200Hz bands. Vowel /o/ and /u/ did not fare as good but still obtained greater than 90% classification rate. 2009-08-13T03:05:58Z 2009-08-13T03:05:58Z 2008-05 Article p.1125-1130 978-1-4244-1691-2 http://ieeexplore.ieee.org/xpls/abs_all.jsp?=&arnumber=4580782 http://hdl.handle.net/123456789/6870 en Proceedings of the International Conference on Computer and Communication Engineering (ICCCE 2008) Institute of Electrical and Electronics Engineering (IEEE)
institution Universiti Malaysia Perlis
building UniMAP Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Perlis
content_source UniMAP Library Digital Repository
url_provider http://dspace.unimap.edu.my/
language English
topic Automatic speech recognition
Speech recognition
Signal classification
Regression analysis
Speech perception
Natural language processing
spellingShingle Automatic speech recognition
Speech recognition
Signal classification
Regression analysis
Speech perception
Natural language processing
Paulraj, M.P.
Sazali, Yaacob
Shahrul Azmi, M. Y.
Vowel classification based on frequency response of vocal tract
description Link to publisher's homepage at http://ieeexplore.ieee.org
format Article
author Paulraj, M.P.
Sazali, Yaacob
Shahrul Azmi, M. Y.
author_facet Paulraj, M.P.
Sazali, Yaacob
Shahrul Azmi, M. Y.
author_sort Paulraj, M.P.
title Vowel classification based on frequency response of vocal tract
title_short Vowel classification based on frequency response of vocal tract
title_full Vowel classification based on frequency response of vocal tract
title_fullStr Vowel classification based on frequency response of vocal tract
title_full_unstemmed Vowel classification based on frequency response of vocal tract
title_sort vowel classification based on frequency response of vocal tract
publisher Institute of Electrical and Electronics Engineering (IEEE)
publishDate 2009
url http://dspace.unimap.edu.my/xmlui/handle/123456789/6870
_version_ 1643788614604161024