The phonetic footprint of Parkinson's disease. (March 2022)

• Record Type: Journal Article
• Authors: Klumpp, Philipp; Arias-Vergara, Tomás; Vásquez-Correa, Juan Camilo; Pérez-Toro, Paula Andrea; Orozco-Arroyave, Juan Rafael; Batliner, Anton; Nöth, Elmar
• Is Part Of: Computer speech & language. Volume 72(2022)
• Publication Date: 2022-03
• Subjects: Parkinson's disease -- Phonetic analysis -- Phoneme recognition -- Pathological speech; Speech processing systems -- Periodicals; Automatic speech recognition -- Periodicals; Computers -- Periodicals; Linguistics -- Periodicals; Speech-Language Pathology -- Periodicals; Traitement automatique de la parole -- Périodiques; Reconnaissance automatique de la parole -- Périodiques; Automatic speech recognition; Speech processing systems; Electronic journals; Periodicals; 006.454
• DOI: 10.1016/j.csl.2021.101321
• Languages: English
• ISSNs: 0885-2308

Abstract

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. Highlights: Deep acoustic models enable phonetic profiling of Parkinson's Disease patients. Distinct differences in phonetic footprints between speaker groups. Intermediate model activations encode information about disease progression. Perceived intelligibility and recognition confidences are correlated in free speech.