Identifying distinctive acoustic and spectral features in Parkinson's disease

Yermiyahu Hauptman, Ruth Aloni-Lavi, Itshak Lapidot, Tanya Gurevich, Yael Manor, Stav Naor, Noa Diamant, Irit Opher

Research output: Contribution to journalConference articlepeer-review

Abstract

In this paper we try to identify spectral and acoustic features that are distinctive of Parkinson's disease patients' speech. We investigate the contribution of several features' families to a simple classification task that distinguishes between two balanced groups - patients with Parkinson's disease and their age and gender matched group of Healthy Controls, both uttering sustained vowels. We achieve over 75% correct classification using a combination of acoustic and spectral features. We show that combining a few statistical functionals of these features yields very good results.. This can be explained by two reasons: the first is that the statistics of Parkinson's disease patients' speech defer from those of Healthy people's speech; the second and more important one is the gradual nature of the Parkinsonian speech that is manifested by the changes within an utterance. We speculate that the feature families that most contribute to the classification task are the most distinctive for detecting the disease and suggest testing this hypothesis by performing long-term analysis of both patient and healthy control subjects. Similar accuracy is obtained when analyzing spontaneous speech where each utterance is represented by a single normalized i-vector.

Keywords

  • Acoustic Features
  • I-vectors
  • Parkinson's Disease
  • Spectral Features

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