Shifted delta cepstral coefficients with RNN to improve the detection of Parkinson’s disease from the speech

Abstract

Parkinson’s disease (PD) is a progressive neurodegenerative disorder of the central nervous system identified by motor and non-motor activities abnormalities. PD affects respiration, laryngeal, articulation, resonance, and prosodic aspects of speech production. Detection of PD from speech is a non-invasive approach useful for automatic screening. Perceptual attributes of speech due to PD are manifested as temporal variations in speech. In this regard, current work investigated the use of LSTM and BiLSTM networks with shifted delta cepstral (SDC) features to detect PD from speech. Further in BiLSTM networks, a multi-head attention mechanism is introduced, assuming that each head captures distinct information to detect PD. SDC features obtained from MFCCs, and SFFCCs are used for developing the PD detection system. The performance of the experiments is validated using the PC-GITA database. The experimental results revealed that BiLSTM networks give a relative improvement of 4-5% over the LSTM networks. The use of a multi-head attention mechanism further improved the detection accuracy of the PD detection system, showing that it can capture various discriminative features.