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
The article has been submitted to a journal for publication.