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Assessment of PD Speech Anomalies @ Home
Dalarna University, School of Technology and Business Studies, Computer Engineering.ORCID iD: 0000-0002-2752-3712
Dalarna University, School of Technology and Business Studies, Computer Engineering.ORCID iD: 0000-0003-0403-338X
2011 (English)In: 15th International Congress of Parkinson's Disease and Movement Disorders, Toronto, Canada, 2011Conference paper (Refereed)
Abstract [en]

Background: Voice processing in real-time is challenging. A drawback of previous work for Hypokinetic Dysarthria (HKD) recognition is the requirement of controlled settings in a laboratory environment. A personal digital assistant (PDA) has been developed for home assessment of PD patients. The PDA offers sound processing capabilities, which allow for developing a module for recognition and quantification HKD. Objective: To compose an algorithm for assessment of PD speech severity in the home environment based on a review synthesis. Methods: 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 are robust to medication changes in Levodopa-responsive patients are investigated for HKD recognition. Keywords such as Hypokinetic Dysarthria , and Speech recognition in real time were used in the search engines. IEEE explorer produced the most useful search hits as compared to Google Scholar, ELIN, EBRARY, PubMed and LIBRIS. Results: Vowel and consonant formants are the most relevant acoustic parameters to reflect PD medication changes. Since relevant speech segments (consonants and vowels) contains minority of speech energy, intelligibility can be improved by amplifying the voice signal using amplitude compression. Pause detection and peak to average power rate calculations for voice segmentation produce rich voice features in real time. Enhancements in voice segmentation can be done by inducing Zero-Crossing rate (ZCR). Consonants have high ZCR whereas vowels have low ZCR. Wavelet transform is found promising for voice analysis since it quantizes non-stationary voice signals over time-series using scale and translation parameters. In this way voice intelligibility in the waveforms can be analyzed in each time frame. Conclusions: This review evaluated HKD recognition algorithms to develop a tool for PD speech home-assessment using modern mobile technology. An algorithm that tackles realtime constraints in HKD recognition based on the review synthesis is proposed. We suggest that speech features may be further processed using wavelet transforms and used with a neural network for detection and quantification of speech anomalies related to PD. Based on this model, patients' speech can be automatically categorized according to UPDRS speech ratings.

Place, publisher, year, edition, pages
Toronto, Canada, 2011.
National Category
Computer and Information Science
Research subject
Komplexa system - mikrodataanalys, E-MOTIONS, Beslutsstöd för Parkinsonbehandling
URN: urn:nbn:se:du-5935ISI: 000291359502293OAI: diva2:522418
15th International Congress of Parkinson's Disease and Movement Disorders , Toronto, Canada, 5-9 juni, 2011
Available from: 2011-09-12 Created: 2011-09-12 Last updated: 2015-06-29Bibliographically approved

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Khan, TahaWestin, Jerker
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