Computational diagnosis of Parkinson's disease directly from natural speech using machine learning techniques

Alex Frid, Hananel Hazan, Dan Hilu, Larry Manevitz, Lorraine O. Ramig, Shimon Sapir

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

57 Scopus citations

Abstract

The human voice signal carries much information in addition to direct linguistic semantic information. This information can be perceived by computational systems. In this work, we show that early diagnosis of Parkinson's disease is possible solely from the voice signal. This is in contrast to earlier work in which we showed that this can be done using hand-calculated features of the speech (such as formants) as annotated by professional speech therapists. In this paper, we review that work and show that a differential diagnosis can be produced directly from the analog speech signal itself. In addition, differentiation can be made between seven different degrees of progression of the disease (including healthy). Such a system can act as an additional stage (or another building block) in a bigger system of natural speech processing. For example it could be used in automatic speech recognition systems that are used as personal assistants (such as Iphones' Siri, Google Voice), or as natural man-machine interfaces. We also conjecture that such systems can be extended to monitoring and classifying additional neurological diseases and speech pathologies. The methods presented here use a combination of signal processing features and machine learning techniques.

Original languageEnglish
Title of host publicationProceedings - 2014 IEEE International Conference on Software Science, Technology and Engineering, SWSTE 2014
PublisherIEEE Computer Society
Pages50-53
Number of pages4
ISBN (Print)9780769551883
DOIs
StatePublished - 2014
Externally publishedYes
Event2014 IEEE International Conference on Software Science, Technology and Engineering, SWSTE 2014 - Ramat Gan, Israel
Duration: 11 Jun 201412 Jun 2014

Publication series

NameProceedings - 2014 IEEE International Conference on Software Science, Technology and Engineering, SWSTE 2014

Conference

Conference2014 IEEE International Conference on Software Science, Technology and Engineering, SWSTE 2014
Country/TerritoryIsrael
CityRamat Gan
Period11/06/1412/06/14

Keywords

  • Classification
  • Machine Learning
  • Natural Speech Analysis
  • Parkinsons disease
  • Support Vector Machine (SVM)

Fingerprint

Dive into the research topics of 'Computational diagnosis of Parkinson's disease directly from natural speech using machine learning techniques'. Together they form a unique fingerprint.

Cite this