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Methods for Detection of Speech Impairment Using Mobile Devices

Fulltext:


Authors:

Taha Kahn, Jerker Westin

Research group:

Artificial Intelligence and Intelligent Systems

Publication Type:

Journal article

Venue:

Recent Patents on Signal Processing

Publisher:

Bentham Science

DOI:

10.2174/2210686311101020163

Abstract

Speech impairment is an important symptom of Parkinson’s disease (PD). This paper presents a detailed systematic literature review on speech impairment assessment through mobile devices. A two-tier review methodology is utilized. The first tier focuses on real-time problems in speech detection. In the second tier, acoustics features that respond to medication changes in Levodopa responsive PD patients are investigated for recognition of speech symptoms. The investigation of the patents reveals that speech disorder assessment can be made by a comparative analysis between pathological acoustic patterns and the normal acoustic patterns saved in a database. The review depicts that vowel and consonant formants are the most relevant acoustic parameters to reflect PD medication changes. Since consonants have high zero-crossing rate (ZCR) whereas vowels have low ZCR, enhancements in voice segmentation can be done by inducing ZCR. Our synthesis further suggests that wavelet transforms have potential for being useful in real-time voice analysis for detection and quantification of symptoms at home.

Bibtex

@article{Kahn3420,
author = {Taha Kahn and Jerker Westin},
title = {Methods for Detection of Speech Impairment Using Mobile Devices},
volume = {2011},
number = {1},
pages = {163--171},
month = {April},
year = {2011},
journal = {Recent Patents on Signal Processing},
publisher = {Bentham Science},
url = {http://www.es.mdu.se/publications/3420-}
}