Voiceprints analysis using MFCC and SVM for detecting patients with Parkinson's disease

Abstract

Parkinson's disease (PD) is a neurodegenerative disorder of unknown etiology. PD patients suffer from hypokinetic dysarthria, which manifests on all aspects of voice production, respiration, phonation, articulation, nasality and prosody. To evaluate these disorders, clinicians have adopted perceptual methods, based on acoustic cues, to distinguish the different disease states. To develop the assessment of voice disorders for detecting patients with Parkinson's disease (PD), we have used a PD dataset of 34 sustained vowel / a /, from 34 people including 17 PD patients. We then extracted from 1 to 20 coefficients of the Mel Frequency Cepstral Coefficients from each person. To extract the voiceprint from each voice sample, we compressed the frames by calculating their average value. For classification, we used Leave-One-Subject-Out validation-scheme along with the Support Vector Machines with its different types of kernels. The best classification accuracy achieved was 91.17% using the first 12 coefficients of the MFCC by Linear kernels SVM.Index Terms-Voice analysis, Parkinson's disease, MFCC, Voiceprint, LOSOVS, SVM.

Voice-Based Detection of Parkinson’s Disease through Ensemble Machine Learning Approach: A Performance Study

“…Achraf Benba et al [21] aimed to separate the people with PD from the control subjects. In their work, the data comprised of 34 sustained vowels, which was collected from 34 people of which 17 were PD subjects.…”

Diagnosis of Parkinson’s Disease based on Wavelet Transform and Mel Frequency Cepstral Coefficients

“…From 10 to 30 ms, this step enables us to obtain, for each speech segment, a quasi-stationary signal. The two adjacent frames are overlapped to avoid abrupt frame-to-frame transitions [12].…”

“…Many researches have also been used on speech characteristics for the diagnosis of Parkinson's disease such as PLP, MFCC and Rasta-PLP [10][11][12][13][14]. Savitha S. Upadhyaa et al [10] worked on the detection of Parkinson's disease from the extraction of MFCCs using the multitaper Thomson windowing technique.…”

“…Orozco -Arroyave et al [11], obtained a 60% accuracy by extracting the MFCC coefficient as recognition accuracy. Achraf Benba et al [12] obtained a percentage accuracy (80%) by the combination of MFCC and the SVM [12], on a database of 17 healthy patients and 17 Parkinson patients.…”

“…In this work, we develop the diagnostic model of Parkinson's disease [12] by the introduction of vocal signal compression of a database [19], which is composed of 18 healthy patients and 20 patients by wavelet transform through the testing of numerous sorts of wavelets, then we will extract the Mel scale cepstral coefficients (MFCC) of the transformed signals, and at the end classification study by vector machine (SVM) which is one of the algorithm of machine learning [20]. We will create a learning base that shows the percentage of 73% of the database and test the entire database to confirm whether patients are ill or not of each wavelet in order to choose the accurate one.…”