Methods for Detection of Speech Impairment Using Mobile Devices

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.