Publications:Parkinson's Disease Detection from Speech Using Convolutional Neural Networks
From ISLAB/CAISR
| 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 |
| Journal | |
| 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 |
| DOI | http://dx.doi.org/10.1007/978-3-319-76111-4_21 |
| Diva url | http://hh.diva-portal.org/smash/record.jsf?searchId=1&pid=diva2:1198161 |
| 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. |
