Depression assessment in people with Parkinson’s disease: The combination of acoustic features and natural language processing

Abstract

Parkinson’s disease produces motor impairments such as bradykinesia, rigidity, and different speech impairments, as same as non-motor symptoms like cognitive decline and depression disturbances. Most studies are focused on the analysis of motor symptoms, and only few works study non-motor impairments. Depression is one of the typical non-motor symptoms developed by many Parkinson’s patients. Impairments in speech production together with depression produce negative effects in the communication capabilities and social interaction of patients. This study proposes a combination of speech analysis and natural language processing methods to extract features from spontaneous speech utterances and their transcripts. We consider state-of-the-art word-embedding methods like Bidirectional Encoder Representations from Transformer (BERT) to process the transcripts, and traditional acoustic features such as Bark band energies and Mel frequency cepstral coefficients to model the speech signals. The features are processed with supervectors generated by Gaussian Mixture Model-Universal Background Model (GMM-UBM) and Support Vector Machine (SVM) classifiers. The dataset consists of 60 Parkinson’s patients divided into two classes according to the depression item of the MDS-UPDRS. The automatic classification of depressed and non-depressed Parkinson’s patients showed F-scores of up to 0.77, which confirms that acoustic and linguistic information embedded in language production can be used for depression analysis in Parkinson’s patients. We present one of the few studies that evaluates depression in Parkinson’s patients considering the combination of acoustic and linguistic information.