The Phonetic Footprint of Parkinson's Disease

TL;DR

This study demonstrates that a phonetic recognizer trained only on healthy speech can detect Parkinson's disease-related speech patterns and correlate with disease severity, suggesting potential for non-pathological training data in PD speech analysis.

Contribution

The paper introduces a neural network-based phonetic recognizer trained solely on healthy speech that can identify Parkinson's disease features without prior exposure to pathological data.

Findings

Detected characteristic PD speech patterns using healthy-trained model

Correlated phonetic prediction confidence with speech intelligibility

Showed pathological data is not necessary for PD speech analysis

Abstract

As one of the most prevalent neurodegenerative disorders, Parkinson's disease (PD) has a significant impact on the fine motor skills of patients. The complex interplay of different articulators during speech production and realization of required muscle tension become increasingly difficult, thus leading to a dysarthric speech. Characteristic patterns such as vowel instability, slurred pronunciation and slow speech can often be observed in the affected individuals and were analyzed in previous studies to determine the presence and progression of PD. In this work, we used a phonetic recognizer trained exclusively on healthy speech data to investigate how PD affected the phonetic footprint of patients. We rediscovered numerous patterns that had been described in previous contributions although our system had never seen any pathological speech previously. Furthermore, we could show that intermediate activations from the neural network could serve as feature vectors encoding information related to the disease state of individuals. We were also able to directly correlate the expert-rated intelligibility of a speaker with the mean confidence of phonetic predictions. Our results support the assumption that pathological data is not necessarily required to train systems that are capable of analyzing PD speech.