Voice based classification of patients with Amyotrophic Lateral Sclerosis, Parkinson's Disease and Healthy Controls with CNN-LSTM using transfer learning

In this paper, we consider 2-class and 3-class classification problems for classifying patients with Amyotrophic Lateral Sclerosis (ALS), Parkinson's Disease (PD), and Healthy Controls (HC) using a CNN-LSTM network. Classification performance is examined for three different tasks, namely, Spontaneous speech (SPON), Diadochokinetic rate (DIDK) and Sustained phoneme production (PHON). Experiments are conducted using speech data recorded from 60 ALS, 60 PD, and 60 HC subjects. Classifications using SVM and DNN are considered as baseline schemes. Classification accuracy of ALS and HC (indicated by ALS/HC) using CNN-LSTM has shown an improvement of 10.40%, 4.22% and 0.08% for PHON, SPON and DIDK tasks, respectively over the best of the baseline schemes. Furthermore, the CNN-LSTM network achieves the highest PD/HC classification accuracy of 88.5% for the SPON task and the highest 3-class (ALS/PD/HC) classification accuracy of 85.24% for the DIDK task. Experiments using transfer learning at low resource training data show that data from ALS benefits PD/HC classification and vice-versa. Experiments with fine-tuning weights of 3-class (ALS/PD/HC) classifier for 2-class classification (PD/HC or ALS/HC) gives an absolute improvement of 2% classification accuracy in SPON task when compared with randomly initialized 2-class classifier.