Automatic Estimation Of Parkinson'S Disease Severity From Diverse Speech Tasks

The need for reliable, scalable and efficient diagnosis of Parkinson's Disease (PD) is a major clinical need. Automating the diagnosis can lead to more accurate and objective predictions as well as provide insights regarding the nature of Parkinson's condition. This paper proposes a fully automated system to rate the severity (UPDRS-III scale) of PD from patients' speech. Specifically, the system captures atypicalities in an individual's voice when performing multiple diverse speaking tasks and makes a unified prediction of the PD severity. The performance is tested in a cross-data setting, with different subjects and dissimilar recording conditions. Results indicate that (i) effective features vary depending on the nature of the specific speech task, (ii) additional novel feature sets to detect distortions in Parkinson's speech significantly improve the prediction accuracy from the Interspeech15 Challenge baseline system and (iii) our fusion system based on an unsupervised clustering technique also improves the accuracy. Our system incorporates i-vector and functionals for segmental features, non-linear time series features, speech rhythm and automatic speech recognition decoding based features. By its application on the Interspeechl 5 eating condition challenge, the system also shows its potential for detecting other sources of speech variability.