A machine learning perspective on the emotional content of Parkinsonian speech

作者:

Highlights:

• We introduce a cross-domain transfer-learning model for speech emotion recognition and apply it to a parkinsonian speech corpus.

• We evaluate whether there is a relationship between Parkinson's condition and emotional scores inferred via our speech emotion recognition model.

• Our model infers more negative emotional characteristics in parkinsonian speech than in healthy speech.

• There is a strong correlation between the severity of the speech impairment and of how sad the parkinsonian speech sounds.

摘要

•We introduce a cross-domain transfer-learning model for speech emotion recognition and apply it to a parkinsonian speech corpus.•We evaluate whether there is a relationship between Parkinson's condition and emotional scores inferred via our speech emotion recognition model.•Our model infers more negative emotional characteristics in parkinsonian speech than in healthy speech.•There is a strong correlation between the severity of the speech impairment and of how sad the parkinsonian speech sounds.

论文关键词:Parkinson's disease,Machine learning,Mixture-of-experts,Speech emotion recognition

论文评审过程:Received 6 March 2020, Revised 26 February 2021, Accepted 29 March 2021, Available online 1 April 2021, Version of Record 19 April 2021.

论文官网地址:https://doi.org/10.1016/j.artmed.2021.102061