Differentiation of speech in Parkinson's disease and spinocerebellar degeneration using deep neural networks.

INTRODUCTION

Assessing dysarthria features in patients with neurodegenerative diseases helps diagnose underlying pathologies. Although deep neural network (DNN) techniques have been widely adopted in various audio processing tasks, few studies have tested whether DNNs can help differentiate neurodegenerative diseases using patients' speech data. This study evaluated whether a DNN model using a transformer architecture could differentiate patients with Parkinson's disease (PD) from patients with spinocerebellar degeneration (SCD) using speech data.

METHODS

Speech data were obtained from 251 and 101 patients with PD and SCD, respectively, while they read a passage. We fine-tuned a pre-trained DNN model using log-mel spectrograms generated from speech data. The DNN model was trained to predict whether the input spectrogram was generated from patients with PD or SCD. We used fivefold cross-validation to evaluate the predictive performance using the area under the receiver operating characteristic curve (AUC) and accuracy, sensitivity, and specificity.

RESULTS

Average ± standard deviation of the AUC, accuracy, sensitivity, and specificity of the trained model for the fivefold cross-validation were 0.93 ± 0.04, 0.87 ± 0.03, 0.83 ± 0.05, and 0.89 ± 0.05, respectively.

CONCLUSION

The DNN model can differentiate speech data of patients with PD from that of patients with SCD with relatively high accuracy and AUC. The proposed method can be used as a non-invasive, easy-to-perform screening method to differentiate PD from SCD using patient speech and is expected to be applied to telemedicine.