Quality Control of Voice Recordings in Remote Parkinson's Disease Monitoring using the Infinite Hidden Markov Model

Amir Hossein Poorjam Alavijeh, Yordan P. Raykov, Reham Badawy, Jesper Rindom Jensen, Mads Græsbøll Christensen, Max A. Little

Research output: Contribution to book/anthology/report/conference proceedingArticle in proceedingResearchpeer-review

6 Citations (Scopus)
221 Downloads (Pure)

Abstract

The performance of voice-based systems for remote monitoring of Parkinson's disease is highly dependent on the degree of adherence of the recordings to the test protocols, which probe for specific symptoms. Identifying segments of the signal that adhere to the protocol assumptions is typically performed manually by experts. This process is costly, time consuming, and often infeasible for large-scale data sets. In this paper, we propose a method to automatically identify the segments of signals that violate the test protocol with a high accuracy. In our approach, the signal is first split into variable duration segments by fitting an infinite hidden Markov model (iHMM) to the frames of the signals in the mel-frequency cepstral domain. The complexity of the iHMM is capable of growing jointly with the data allowing us to infer a potentially large (asymptotically infinite) number of different phenomena segmented into different hidden states. Then, we identify the segments that adhere to the test protocol by applying a multinomial naive Bayes classifier to the state indicators of segments. The experimental results show that even by using a small amount of training data, we can achieve around 96% accuracy in identifying short-term protocol violations with a 0.2 s resolution.
Original languageEnglish
Title of host publication2019 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Proceedings
Number of pages5
PublisherIEEE
Publication dateMay 2019
Pages805-809
Article number8682523
ISBN (Electronic)9781479981311
DOIs
Publication statusPublished - May 2019
Event2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) - Brighton, United Kingdom
Duration: 12 May 201917 May 2019
https://2019.ieeeicassp.org/

Conference

Conference2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Country/TerritoryUnited Kingdom
CityBrighton
Period12/05/201917/05/2019
Internet address
SeriesI E E E International Conference on Acoustics, Speech and Signal Processing. Proceedings
ISSN1520-6149

Keywords

  • Bayesian Nonparametric
  • Infinite HMM
  • Parkinson's Disease
  • Quality Control
  • Segmentation

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