A forced gaussians based methodology for the differential evaluation of Parkinson's Disease by means of speech processing. (February 2019)

• Record Type: Journal Article
• Authors: Moro-Velazquez, Laureano; Gomez-Garcia, Jorge Andres; Godino-Llorente, Juan Ignacio; Villalba, Jesús; Rusz, Jan; Shattuck-Hufnagel, Stephanie; Dehak, Najim
• Is Part Of: Biomedical signal processing and control. Volume 48(2019)
• Page Start: 205
• Page End: 220
• Publication Date: 2019-02
• Subjects: Speech processing -- Machine learning -- Parkinson's Disease -- Phoneme -- Forced alignment; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2018.10.020
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: The Forced gaussian schemes allow to compare phonemes between different classes. PD influences phonemes requiring a higher narrowing of the vocal tract. The glass ceiling of accuracy in the detection of PD from speech is below 95%. Cross-corpora validation is highly recommended to ensure generalization. Abstract: Literature evidences the existence of hypokinetic dysarthria in parkinsonian patients and, consequently, the objective characterization of the dysarthric signs associated to the articulatory aspect of speech can be used to detect Parkinson's Disease (PD) providing clinicians with new tools to support the clinical diagnosis. However, no work has analyzed in detail the importance of the different phonemes in the automatic detection of PD from the speech. This work proposes new approaches for this detection by using new classification schemes that allow to compare independently the different phonetic units of patients and controls employed during several speech tasks. Three different parkinsonian corpora were used allowing cross-validation and cross-corpora trials. The results of cross-validation trials (k-folds) provided accuracies between 81% and 94%, with AUC between 0.87 and 0.97 depending on the corpus, while cross-corpora trials yielded accuracies between 66% and 76% with AUC between 0.76 and 0.87. These results suggest that PD affects to the articulatory sequence as a whole, influencing more clearly phonetic units requiring a higher narrowing of the vocal tract. Additionally, text-dependent utterances are considered as the recommended speech task for the detection of PD in this type of schemes as these allow to compare more precisely the phonetic units of patients and controls. Lastly, this work discusses the existence of a glass ceiling in the accuracy of the systems for the automatic detection of PD using speech, concluding that this is below 95% for most of the cases.