Methods for Detection of Speech Impairment Using Mobile Devices
Taha Khan and Jerker Westin
Affiliation: Academy of Industry and Society, Computer Engineering, Dalarna University, SE- 781 88 Borlange, Sweden.
Keywords: Parkinson's disease, hypokinetic dysarthria, voice recognition, speech impairment, telemedicine, Mobile Devices, Acoustics, Vowel Formant Frequency, neural network, artificial neural network, Wavelet Analysis, Discrete Wavelets Transforms, biofeedback systems, speech disorder analysis, LSVT therapy
Speech impairment is an important symptom of Parkinsons 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.
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