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Isolated Word Recognition for Dysarthric Patients

by Sheena Christabel Pravin, Abhiroop Chellu, P. Kannan
Communications on Applied Electronics
Foundation of Computer Science (FCS), NY, USA
Volume 5 - Number 2
Year of Publication: 2016
Authors: Sheena Christabel Pravin, Abhiroop Chellu, P. Kannan

Sheena Christabel Pravin, Abhiroop Chellu, P. Kannan . Isolated Word Recognition for Dysarthric Patients. Communications on Applied Electronics. 5, 2 ( May 2016), 14-17. DOI=10.5120/cae2016652219

@article{ 10.5120/cae2016652219,
author = { Sheena Christabel Pravin, Abhiroop Chellu, P. Kannan },
title = { Isolated Word Recognition for Dysarthric Patients },
journal = { Communications on Applied Electronics },
issue_date = { May 2016 },
volume = { 5 },
number = { 2 },
month = { May },
year = { 2016 },
issn = { 2394-4714 },
pages = { 14-17 },
numpages = {9},
url = { },
doi = { 10.5120/cae2016652219 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
%0 Journal Article
%1 2023-09-04T19:54:38.558337+05:30
%A Sheena Christabel Pravin
%A Abhiroop Chellu
%A P. Kannan
%T Isolated Word Recognition for Dysarthric Patients
%J Communications on Applied Electronics
%@ 2394-4714
%V 5
%N 2
%P 14-17
%D 2016
%I Foundation of Computer Science (FCS), NY, USA

In this paper, a HMM based speech recognition system is proposed for the Dysarthric patients. The speech samples recorded from patients with Spastic Dysarthria with mid to high intelligibility are taken from the UA Research database. The speech samples are de-noised using Discrete Wavelet Transform (DWT), after which the MFCC, LPC features are extracted. A comparative study on speech recognition using MFCC and LPC are presented. The recognition efficiency is found to be 68.50% using MFCC features and 66.54% using LPC features.

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Index Terms

Computer Science
Information Sciences