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Running-speech MFCC are better markers of Parkinsonian speech deficits than vowel phonation and diadochokinetic
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems. Dalarna University.
(English)Manuscript (preprint) (Other academic)
Abstract [en]

Background:The mel-frequency cepstral coefficients (MFCC) are relied for their capability to identify pathological speech. The literature suggests that triangular mel-filters that are used in the MFCC calculation provide an approximation of the human auditory perception. This approximation allows quantifying the clinician’s perception of the intelligibility of the patient’s speech that allows mapping between the clinician’s score of the severity of speech symptoms and the actual symptom severity of the patient’s speech. Previous research on speech impairment in Parkinson’s disease(PD) used sustained-phonation and diadochokinesis tests to score symptoms using the unified Parkinson’s disease rating scale motor speech examination (UPDRS-S).

Objectives:The paper aims to utilize MFCC computed from the recordings of running speech examination for classification of the severity of speech symptoms based on the UPDRS-S. The secondary aim was to compare the performance of the MFCC from running-speech, and the MFCC from sustained-phonation and diadochokinesis recordings, in classifying the UPDRS-S levels.

Method:The study involved audio recordings of motor speech examination of 80 subjects, including 60 PD patients and 20 normal controls. Three different running-speech tests, four different sustained-phonation tests and two different diadochokinesis tests were recorded in different occasions from each subject. The vocal performance of each subject was rated by a clinician using the UPDRS-S. A total of 16 MFCC computed separately from the recordings of running-speech, sustained-phonation and diadochokinesis tests were used to train a support vector machine (SVM) for classifying the levels of UPDRS-S severity. The area under the ROC curve (AoC) was used to compare the feasibility of classification models. Additionally, the Guttman correlation coefficient (μ2) and intra-class correlation coefficient (ICC) were used for feature validation.

Results:The experiments on the SVM trained using the MFCC from running-speech samples produced higher AoC (84%and 85%) in classifying the severity levels of UPDRS-S as compared to the AoC produced by the MFCC from sustained-phonation (88% and 77%) and diadochokinesis (77% and 77%) samples in 10-fold cross validation and training-testing schemes respectively. The μ2 between the MFCC from running speech samples and clinical ratings was stronger (μ2 up to 0.7) than the μ2 between the clinical ratings and the MFCC from sustained-phonation and diadochokinesis samples. The ICC of the MFCC from the running-speech samples recorded in different test occasions was stronger as compared to the ICC of the MFCC from sustained-phonation and diadochokinesis samples recorded in different test occasions.

Conclusions:The strong classification ability of running-speech MFCC and SVM, on one hand, supports suitability of this scheme to monitor speech symptoms in PD. Besides, the values of μ2 and ICC suggest that the MFCC from running speech signals are more reliable for scoring speech symptoms as compared to the MFCC from sustained-phonation and diadochokinesis signals

National Category
Engineering and Technology
Research subject
Care Sciences
URN: urn:nbn:se:mdh:diva-24645OAI: diva2:705196
Knowledge Foundation

The article has been submitted to a journal for publication.

Available from: 2014-03-14 Created: 2014-03-14 Last updated: 2015-07-15Bibliographically approved
In thesis
1. First-principle data-driven models for assessment of motor disorders in Parkinson’s disease
Open this publication in new window or tab >>First-principle data-driven models for assessment of motor disorders in Parkinson’s disease
2014 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Parkinson’s disease (PD) is an increasing neurological disorder in an aging society. The motor and non-motor symptoms of PD advance with the disease progression and occur in varying frequency and duration. In order to affirm the full extent of a patient’s condition, repeated assessments are necessary to adjust medical prescription. In clinical studies, symptoms are assessed using the unified Parkinson’s disease rating scale (UPDRS). On one hand, the subjective rating using UPDRS relies on clinical expertise. On the other hand, it requires the physical presence of patients in clinics which implies high logistical costs. Another limitation of clinical assessment is that the observation in hospital may not accurately represent a patient’s situation at home. For such reasons, the practical frequency of tracking PD symptoms may under-represent the true time scale of PD fluctuations and may result in an overall inaccurate assessment. Current technologies for at-home PD treatment are based on data-driven approaches for which the interpretation and reproduction of results are problematic. 

The overall objective of this thesis is to develop and evaluate unobtrusive computer methods for enabling remote monitoring of patients with PD. It investigates first-principle data-driven model based novel signal and image processing techniques for extraction of clinically useful information from audio recordings of speech (in texts read aloud) and video recordings of gait and finger-tapping motor examinations. The aim is to map between PD symptoms severities estimated using novel computer methods and the clinical ratings based on UPDRS part-III (motor examination). A web-based test battery system consisting of self-assessment of symptoms and motor function tests was previously constructed for a touch screen mobile device. A comprehensive speech framework has been developed for this device to analyze text-dependent running speech by: (1) extracting novel signal features that are able to represent PD deficits in each individual component of the speech system, (2) mapping between clinical ratings and feature estimates of speech symptom severity, and (3) classifying between UPDRS part-III severity levels using speech features and statistical machine learning tools. A novel speech processing method called cepstral separation difference showed stronger ability to classify between speech symptom severities as compared to existing features of PD speech. In the case of finger tapping, the recorded videos of rapid finger tapping examination were processed using a novel computer-vision (CV) algorithm that extracts symptom information from video-based tapping signals using motion analysis of the index-finger which incorporates a face detection module for signal calibration. This algorithm was able to discriminate between UPDRS part III severity levels of finger tapping with high classification rates. Further analysis was performed on novel CV based gait features constructed using a standard human model to discriminate between a healthy gait and a Parkinsonian gait.

The findings of this study suggest that the symptom severity levels in PD can be discriminated with high accuracies by involving a combination of first-principle (features) and data-driven (classification) approaches. The processing of audio and video recordings on one hand allows remote monitoring of speech, gait and finger-tapping examinations by the clinical staff. On the other hand, the first-principles approach eases the understanding of symptom estimates for clinicians. We have demonstrated that the selected features of speech, gait and finger tapping were able to discriminate between symptom severity levels, as well as, between healthy controls and PD patients with high classification rates. The findings support suitability of these methods to be used as decision support tools in the context of PD assessment.

Place, publisher, year, edition, pages
Sweden: Mälardalen University, 2014. 102 p.
Mälardalen University Press Dissertations, ISSN 1651-4238 ; 153
National Category
Engineering and Technology
Research subject
Computer Science
urn:nbn:se:mdh:diva-24647 (URN)978-91-7485-142-7 (ISBN)
Public defence
2014-04-16, Clas Ohlson, Studenternas Hus Tenoren, Campus Borlänge, 13:00 (English)
Knowledge Foundation
Available from: 2014-03-17 Created: 2014-03-14 Last updated: 2015-07-15Bibliographically approved

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