Publications:Parkinson's Disease Detection from Speech Using Convolutional Neural Networks


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Title Parkinson’s Disease Detection from Speech Using Convolutional Neural Networks
Author Evaldas Vaiciukynas and Adas Gelzinis and Antanas Verikas and Marija Bacauskiene
Year 2018
PublicationType Conference Paper
HostPublication Smart objects and technologies for social good : Third International Conference, GOODTECHS 2017, Pisa, Italy, November 29-30, 2017, Proceedings
Conference Third EAI International Conference on Smart Objects and Technologies for Social Good, GOODTECHS 2017, Pisa, Italy, November 29-30, 2017
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Abstract 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. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2018.