Vocal modulation features in the prediction of major depressive disorder severity
Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2014.
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| Format: | Thesis |
| Language: | eng |
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Massachusetts Institute of Technology
2015
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| Online Access: | http://hdl.handle.net/1721.1/93072 |
| _version_ | 1811072970844536832 |
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| author | Horwitz-Martin, Rachelle (Rachelle Laura) |
| author2 | Thomas F. Quatieri. |
| author_facet | Thomas F. Quatieri. Horwitz-Martin, Rachelle (Rachelle Laura) |
| author_sort | Horwitz-Martin, Rachelle (Rachelle Laura) |
| collection | MIT |
| description | Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2014. |
| first_indexed | 2024-09-23T09:25:17Z |
| format | Thesis |
| id | mit-1721.1/93072 |
| institution | Massachusetts Institute of Technology |
| language | eng |
| last_indexed | 2024-09-23T09:25:17Z |
| publishDate | 2015 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/930722019-04-12T15:24:39Z Vocal modulation features in the prediction of major depressive disorder severity Horwitz-Martin, Rachelle (Rachelle Laura) Thomas F. Quatieri. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science. Electrical Engineering and Computer Science. Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2014. "September 2014." Cataloged from PDF version of thesis. Includes bibliographical references (pages 113-115). This thesis develops a model of vocal modulations up to 50 Hz in sustained vowels as a basis for biomarkers of neurological disease, particularly Major Depressive Disorder (MDD). Two model components contribute to amplitude modulation (AM): AM from respiratory muscles and from interaction between formants and frequency modulation in the fundamental frequency harmonics. Based on the modulation model, we test three methods to extract the envelope of the third formant from which features are extracted using sustained vowels from the 2013 AudioNisual Emotion Challenge. Using a Gaussian-Mixture-Model-based predictor, we evaluate performance of each feature in predicting subjects' Beck MDD severity score by the root mean square error (RMSE), mean absolute error (MAE), and Spearman correlation between the actual Beck score and predicted score. Our lowest MAE and RMSE values are 8.46 and 10.32, respectively (Spearman correlation=0.487, p<0.001), relative to the mean MAE of 10.05 and mean RMSE of 11.86. by Rachelle L. Horwitz. S.M. 2015-01-20T18:00:10Z 2015-01-20T18:00:10Z 2014 Thesis http://hdl.handle.net/1721.1/93072 900010113 eng M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission. http://dspace.mit.edu/handle/1721.1/7582 115 pages application/pdf Massachusetts Institute of Technology |
| spellingShingle | Electrical Engineering and Computer Science. Horwitz-Martin, Rachelle (Rachelle Laura) Vocal modulation features in the prediction of major depressive disorder severity |
| title | Vocal modulation features in the prediction of major depressive disorder severity |
| title_full | Vocal modulation features in the prediction of major depressive disorder severity |
| title_fullStr | Vocal modulation features in the prediction of major depressive disorder severity |
| title_full_unstemmed | Vocal modulation features in the prediction of major depressive disorder severity |
| title_short | Vocal modulation features in the prediction of major depressive disorder severity |
| title_sort | vocal modulation features in the prediction of major depressive disorder severity |
| topic | Electrical Engineering and Computer Science. |
| url | http://hdl.handle.net/1721.1/93072 |
| work_keys_str_mv | AT horwitzmartinrachellerachellelaura vocalmodulationfeaturesinthepredictionofmajordepressivedisorderseverity |