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Effect of acoustic conditions on algorithms to detect Parkinson's disease from speech | IEEE Conference Publication | IEEE Xplore

Effect of acoustic conditions on algorithms to detect Parkinson's disease from speech

Publisher: IEEE

Abstract:

Automatic detection of Parkinson's disease (PD) from speech is a basic step towards computer-aided tools supporting the diagnosis and monitoring of the disease. Although ...View more

Abstract:

Automatic detection of Parkinson's disease (PD) from speech is a basic step towards computer-aided tools supporting the diagnosis and monitoring of the disease. Although several methods have been proposed, their applicability to real-world situations is still unclear. In particular, the effect of acoustic conditions is not well understood. In this paper, the effects on the accuracy of five different methods to detect PD from speech are evaluated. Among the considered conditions, background noise produces the worst effect, while dynamic compression or some speech codecs can even have a marginal positive impact. We also consider, for the first time in this context, the problem of mismatches, i.e., when train/test acoustic conditions are different, and observe a high negative impact on all considered methods. Overall, this study is a step forward in performing a continuous monitoring of the neurological state of the patients in non-controlled acoustic conditions.
Date of Conference: 05-09 March 2017
Date Added to IEEE Xplore: 19 June 2017
ISBN Information:
Electronic ISSN: 2379-190X
Publisher: IEEE
Conference Location: New Orleans, LA, USA

References

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