Publications:Fusing Various Audio Feature Sets for Detection of Parkinson's Disease from Sustained Voice and Speech Recordings
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| Title | Fusing Various Audio Feature Sets for Detection of Parkinson’s Disease from Sustained Voice and Speech Recordings |
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| Author | |
| Year | 2016 |
| PublicationType | Journal Paper |
| Journal | Lecture Notes in Computer Science |
| HostPublication | |
| Conference | 18th International Conference, SPECOM 2016, Budapest, Hungary, August 23-27, 2016 |
| DOI | http://dx.doi.org/10.1007/978-3-319-43958-7_39 |
| Diva url | http://hh.diva-portal.org/smash/record.jsf?searchId=1&pid=diva2:955931 |
| Abstract | The aim of this study is the analysis of voice and speech recordings for the task of Parkinson’s disease detection. Voice modality corresponds to sustained phonation /a/ and speech modality to a short sentence in Lithuanian language. Diverse information from recordings is extracted by 22 well-known audio feature sets. Random forest is used as a learner, both for individual feature sets and for decision-level fusion. Essentia descriptors were found as the best individual feature set, achieving equal error rate of 16.3 % for voice and 13.3 % for speech. Fusion of feature sets and modalities improved detection and achieved equal error rate of 10.8 %. Variable importance in fusion revealed speech modality as more important than voice. © Springer International Publishing Switzerland 2016 |
