Vocal fold pathology assessment using PCA and LDA

Jennifer C. Saldanha, T. Ananthakrishna, Rohan Pinto

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

It is possible to identify voice disorders using certain features of speech signals. A complementary technique could be acoustic analysis of the speech signal, which is shown to be a potentially useful tool to detect voice diseases. The focus of this study is to formulate a speech parameter estimation algorithm for analysis and detection of vocal fold pathology and also bring out scale to measure severity of the disease. The speech processing algorithm proposed estimates features necessary to formulate a stochastic model to characterize healthy and pathology conditions from speech recordings. Speech signal features such as MFCC are extracted from acoustic analysis of voiced speech of normal and pathological subjects. A principal component analysis with minimum distance classifier (PCA+MDC) and linear discriminant analysis (LDA) classifier are designed and the classification results have been reported.

Original languageEnglish
Title of host publication2013 International Conference on Intelligent Systems and Signal Processing, ISSP 2013
Pages140-144
Number of pages5
DOIs
Publication statusPublished - 2013
Event2013 International Conference on Intelligent Systems and Signal Processing, ISSP 2013 - Vallabh Vidyanagar, Anand, Gujarat, India
Duration: 01-03-201302-03-2013

Conference

Conference2013 International Conference on Intelligent Systems and Signal Processing, ISSP 2013
CountryIndia
CityVallabh Vidyanagar, Anand, Gujarat
Period01-03-1302-03-13

Fingerprint

Discriminant analysis
Pathology
Classifiers
Acoustics
Speech processing
Stochastic models
Parameter estimation
Principal component analysis

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Signal Processing

Cite this

Saldanha, J. C., Ananthakrishna, T., & Pinto, R. (2013). Vocal fold pathology assessment using PCA and LDA. In 2013 International Conference on Intelligent Systems and Signal Processing, ISSP 2013 (pp. 140-144). [6526890] https://doi.org/10.1109/ISSP.2013.6526890
Saldanha, Jennifer C. ; Ananthakrishna, T. ; Pinto, Rohan. / Vocal fold pathology assessment using PCA and LDA. 2013 International Conference on Intelligent Systems and Signal Processing, ISSP 2013. 2013. pp. 140-144
@inproceedings{684ed5b9628049da94493304ca7a5165,
title = "Vocal fold pathology assessment using PCA and LDA",
abstract = "It is possible to identify voice disorders using certain features of speech signals. A complementary technique could be acoustic analysis of the speech signal, which is shown to be a potentially useful tool to detect voice diseases. The focus of this study is to formulate a speech parameter estimation algorithm for analysis and detection of vocal fold pathology and also bring out scale to measure severity of the disease. The speech processing algorithm proposed estimates features necessary to formulate a stochastic model to characterize healthy and pathology conditions from speech recordings. Speech signal features such as MFCC are extracted from acoustic analysis of voiced speech of normal and pathological subjects. A principal component analysis with minimum distance classifier (PCA+MDC) and linear discriminant analysis (LDA) classifier are designed and the classification results have been reported.",
author = "Saldanha, {Jennifer C.} and T. Ananthakrishna and Rohan Pinto",
year = "2013",
doi = "10.1109/ISSP.2013.6526890",
language = "English",
isbn = "9781479903160",
pages = "140--144",
booktitle = "2013 International Conference on Intelligent Systems and Signal Processing, ISSP 2013",

}

Saldanha, JC, Ananthakrishna, T & Pinto, R 2013, Vocal fold pathology assessment using PCA and LDA. in 2013 International Conference on Intelligent Systems and Signal Processing, ISSP 2013., 6526890, pp. 140-144, 2013 International Conference on Intelligent Systems and Signal Processing, ISSP 2013, Vallabh Vidyanagar, Anand, Gujarat, India, 01-03-13. https://doi.org/10.1109/ISSP.2013.6526890

Vocal fold pathology assessment using PCA and LDA. / Saldanha, Jennifer C.; Ananthakrishna, T.; Pinto, Rohan.

2013 International Conference on Intelligent Systems and Signal Processing, ISSP 2013. 2013. p. 140-144 6526890.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

TY - GEN

T1 - Vocal fold pathology assessment using PCA and LDA

AU - Saldanha, Jennifer C.

AU - Ananthakrishna, T.

AU - Pinto, Rohan

PY - 2013

Y1 - 2013

N2 - It is possible to identify voice disorders using certain features of speech signals. A complementary technique could be acoustic analysis of the speech signal, which is shown to be a potentially useful tool to detect voice diseases. The focus of this study is to formulate a speech parameter estimation algorithm for analysis and detection of vocal fold pathology and also bring out scale to measure severity of the disease. The speech processing algorithm proposed estimates features necessary to formulate a stochastic model to characterize healthy and pathology conditions from speech recordings. Speech signal features such as MFCC are extracted from acoustic analysis of voiced speech of normal and pathological subjects. A principal component analysis with minimum distance classifier (PCA+MDC) and linear discriminant analysis (LDA) classifier are designed and the classification results have been reported.

AB - It is possible to identify voice disorders using certain features of speech signals. A complementary technique could be acoustic analysis of the speech signal, which is shown to be a potentially useful tool to detect voice diseases. The focus of this study is to formulate a speech parameter estimation algorithm for analysis and detection of vocal fold pathology and also bring out scale to measure severity of the disease. The speech processing algorithm proposed estimates features necessary to formulate a stochastic model to characterize healthy and pathology conditions from speech recordings. Speech signal features such as MFCC are extracted from acoustic analysis of voiced speech of normal and pathological subjects. A principal component analysis with minimum distance classifier (PCA+MDC) and linear discriminant analysis (LDA) classifier are designed and the classification results have been reported.

UR - http://www.scopus.com/inward/record.url?scp=84880197883&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84880197883&partnerID=8YFLogxK

U2 - 10.1109/ISSP.2013.6526890

DO - 10.1109/ISSP.2013.6526890

M3 - Conference contribution

SN - 9781479903160

SP - 140

EP - 144

BT - 2013 International Conference on Intelligent Systems and Signal Processing, ISSP 2013

ER -

Saldanha JC, Ananthakrishna T, Pinto R. Vocal fold pathology assessment using PCA and LDA. In 2013 International Conference on Intelligent Systems and Signal Processing, ISSP 2013. 2013. p. 140-144. 6526890 https://doi.org/10.1109/ISSP.2013.6526890