Automatic detection of Parkinsonian speech using wavelet scattering features.

In this paper, we study the automatic detection of Parkinson's disease (PD) from speech using features computed by a two-layer wavelet scattering network, which generates locally stable and translation-invariant features at each layer. The scattering features are encoded using Fisher vectors to obtain a single fixed-size feature vector per utterance. Support vector machine and feed-forward neural network classifiers are trained using the utterance-level features to perform the detection task (healthy vs PD). The results obtained with the PC-GITA database revealed that the proposed approach shows better results in comparison to the state-of-the-art techniques. The best classification accuracy of 87% was achieved with the proposed approach using speech from a text reading task.