Comparative Study of Speech Analysis Methods to Predict Parkinson's Disease

Adedolapo Aishat Toye, Suryaprakash Kompalli

Comparative Study of Speech Analysis Methods to Predict Parkinson's Disease

Adedolapo Aishat Toye, Suryaprakash Kompalli

Abstract: One of the symptoms observed in the early stages of Parkinson's Disease (PD) is speech impairment. Speech disorders can be used to detect this disease before it degenerates. This work analyzes speech features and machine learning approaches to predict PD. Acoustic features such as shimmer and jitter variants, and Mel Frequency Cepstral Coefficients (MFCC) are extracted from speech signals. We use two datasets in this work: the MDVR-KCL and Italian ParkinsonÕs Voice and Speech database. To separate PD and non-PD speech signals, seven classification models were implemented: K-Nearest Neighbor, Decision Trees, Support Vector Machines, Naive Bayes, Logistic Regression, Gradient Boosting, Random Forests. Three feature sets were used for each of the models: (a) Acoustic features only, (b) All the acoustic features and MFCC, (c) Selected subset of features from acoustic features and MFCC. Using all the acoustic features and MFCC, together with SVM produced the highest performance with an accuracy of 98% and F1-Score of 99%. When compared with prior art, this shows a better performance. Our code and related documentation is available in a public domain repository.

Poster

Abstract: One of the symptoms observed in the early stages of Parkinson's Disease (PD) is speech impairment. Speech disorders can be used to detect this disease before it degenerates. This work analyzes speech features and machine learning approaches to predict PD. Acoustic features such as shimmer and jitter variants, and Mel Frequency Cepstral Coefficients (MFCC) are extracted from speech signals. We use two datasets in this work: the MDVR-KCL and Italian ParkinsonÕs Voice and Speech database. To separate PD and non-PD speech signals, seven classification models were implemented: K-Nearest Neighbor, Decision Trees, Support Vector Machines, Naive Bayes, Logistic Regression, Gradient Boosting, Random Forests. Three feature sets were used for each of the models: (a) Acoustic features only, (b) All the acoustic features and MFCC, (c) Selected subset of features from acoustic features and MFCC. Using all the acoustic features and MFCC, together with SVM produced the highest performance with an accuracy of 98% and F1-Score of 99%. When compared with prior art, this shows a better performance. Our code and related documentation is available in a public domain repository.