Application of deep learning tends to outperform hand-crafted features in many domains. This study uses convolutional neural networks to explore effectiveness of various segments of a speech signal,? – text-dependent pronunciation of a short sentence, – in Parkinson’s disease detection task. Besides the common Mel-frequency spectrogram and its first and second derivatives, inclusion of various other input feature maps is also considered. Image interpolation is investigated as a solution to obtain a spectrogram of fixed length. The equal error rate (EER) for sentence segments varied from 20.3% to 29.5%. Fusion of decisions from sentence segments achieved EER of 14.1%, whereas the best result when using the full sentence exhibited EER of 16.8%. Therefore, splitting speech into segments could be recommended for Parkinson’s disease detection.
CITATION STYLE
Vaiciukynas, E., Gelzinis, A., Verikas, A., & Bacauskiene, M. (2018). Parkinson’s Disease Detection from Speech Using Convolutional Neural Networks. In Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST (Vol. 233, pp. 206–215). Springer Verlag. https://doi.org/10.1007/978-3-319-76111-4_21
Mendeley helps you to discover research relevant for your work.