Classification and analysis of speech abnormalities

Jagdish Nayak, P. S. Bhat, R. Acharya, U. V. Aithal

Research output: Contribution to journalArticle

23 Citations (Scopus)

Abstract

Analysis of speech has become a popular non-invasive tool for assessing the speech abnormalities. Acoustic nature of the abnormal speech gives relevant information about the type of disorder in the speech production system. These signals are essentially non-stationary; may contain indicators of current disease, or even warnings about impending diseases. The indicators may be present at all times or may occur at random -:during certain intervals of the day. However, to study and pinpoint abnormalities in voluminous data collected over several hours is strenuous and time consuming. Therefore, computer based analytical tools for in-depth study and classification of data over daylong intervals can be very useful in diagnostics. This paper deals with the classification of certain diseases using artificial neural network, and then analyzed. This analysis is carried out using continuous wavelet transformation patterns. The results for various types of subjects discussed in detail and it is evident that the classifier presented in this paper has a remarkable efficiency in the range of 80-85% of accuracy.

Original languageEnglish
Pages (from-to)319-327
Number of pages9
JournalITBM-RBM
Volume26
Issue number5-6
DOIs
Publication statusPublished - 01-10-2005

Fingerprint

Speech Disorders
Acoustics
Classifiers
Neural networks

All Science Journal Classification (ASJC) codes

  • Biophysics

Cite this

Nayak, Jagdish ; Bhat, P. S. ; Acharya, R. ; Aithal, U. V. / Classification and analysis of speech abnormalities. In: ITBM-RBM. 2005 ; Vol. 26, No. 5-6. pp. 319-327.
@article{111935eca36a42688afea9d99aaeca76,
title = "Classification and analysis of speech abnormalities",
abstract = "Analysis of speech has become a popular non-invasive tool for assessing the speech abnormalities. Acoustic nature of the abnormal speech gives relevant information about the type of disorder in the speech production system. These signals are essentially non-stationary; may contain indicators of current disease, or even warnings about impending diseases. The indicators may be present at all times or may occur at random -:during certain intervals of the day. However, to study and pinpoint abnormalities in voluminous data collected over several hours is strenuous and time consuming. Therefore, computer based analytical tools for in-depth study and classification of data over daylong intervals can be very useful in diagnostics. This paper deals with the classification of certain diseases using artificial neural network, and then analyzed. This analysis is carried out using continuous wavelet transformation patterns. The results for various types of subjects discussed in detail and it is evident that the classifier presented in this paper has a remarkable efficiency in the range of 80-85{\%} of accuracy.",
author = "Jagdish Nayak and Bhat, {P. S.} and R. Acharya and Aithal, {U. V.}",
year = "2005",
month = "10",
day = "1",
doi = "10.1016/j.rbmret.2005.05.002",
language = "English",
volume = "26",
pages = "319--327",
journal = "IRBM",
issn = "1959-0318",
publisher = "Elsevier Masson",
number = "5-6",

}

Classification and analysis of speech abnormalities. / Nayak, Jagdish; Bhat, P. S.; Acharya, R.; Aithal, U. V.

In: ITBM-RBM, Vol. 26, No. 5-6, 01.10.2005, p. 319-327.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Classification and analysis of speech abnormalities

AU - Nayak, Jagdish

AU - Bhat, P. S.

AU - Acharya, R.

AU - Aithal, U. V.

PY - 2005/10/1

Y1 - 2005/10/1

N2 - Analysis of speech has become a popular non-invasive tool for assessing the speech abnormalities. Acoustic nature of the abnormal speech gives relevant information about the type of disorder in the speech production system. These signals are essentially non-stationary; may contain indicators of current disease, or even warnings about impending diseases. The indicators may be present at all times or may occur at random -:during certain intervals of the day. However, to study and pinpoint abnormalities in voluminous data collected over several hours is strenuous and time consuming. Therefore, computer based analytical tools for in-depth study and classification of data over daylong intervals can be very useful in diagnostics. This paper deals with the classification of certain diseases using artificial neural network, and then analyzed. This analysis is carried out using continuous wavelet transformation patterns. The results for various types of subjects discussed in detail and it is evident that the classifier presented in this paper has a remarkable efficiency in the range of 80-85% of accuracy.

AB - Analysis of speech has become a popular non-invasive tool for assessing the speech abnormalities. Acoustic nature of the abnormal speech gives relevant information about the type of disorder in the speech production system. These signals are essentially non-stationary; may contain indicators of current disease, or even warnings about impending diseases. The indicators may be present at all times or may occur at random -:during certain intervals of the day. However, to study and pinpoint abnormalities in voluminous data collected over several hours is strenuous and time consuming. Therefore, computer based analytical tools for in-depth study and classification of data over daylong intervals can be very useful in diagnostics. This paper deals with the classification of certain diseases using artificial neural network, and then analyzed. This analysis is carried out using continuous wavelet transformation patterns. The results for various types of subjects discussed in detail and it is evident that the classifier presented in this paper has a remarkable efficiency in the range of 80-85% of accuracy.

UR - http://www.scopus.com/inward/record.url?scp=29044442043&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=29044442043&partnerID=8YFLogxK

U2 - 10.1016/j.rbmret.2005.05.002

DO - 10.1016/j.rbmret.2005.05.002

M3 - Article

VL - 26

SP - 319

EP - 327

JO - IRBM

JF - IRBM

SN - 1959-0318

IS - 5-6

ER -