Automatic voice disorder recognition using acoustic amplitude modulation features
Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2004.
| Main Author: | |
|---|---|
| Other Authors: | |
| Format: | Thesis |
| Language: | eng |
| Published: |
Massachusetts Institute of Technology
2006
|
| Subjects: | |
| Online Access: | http://hdl.handle.net/1721.1/30092 |
| _version_ | 1826198348538839040 |
|---|---|
| author | Malyska, Nicolas, 1977- |
| author2 | Thomas F. Quatieri. |
| author_facet | Thomas F. Quatieri. Malyska, Nicolas, 1977- |
| author_sort | Malyska, Nicolas, 1977- |
| collection | MIT |
| description | Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2004. |
| first_indexed | 2024-09-23T11:03:26Z |
| format | Thesis |
| id | mit-1721.1/30092 |
| institution | Massachusetts Institute of Technology |
| language | eng |
| last_indexed | 2024-09-23T11:03:26Z |
| publishDate | 2006 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/300922019-04-11T14:36:28Z Automatic voice disorder recognition using acoustic amplitude modulation features Malyska, Nicolas, 1977- Thomas F. Quatieri. Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science. Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science. Electrical Engineering and Computer Science. Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2004. Includes bibliographical references (p. 114-117). An automatic dysphonia recognition system is designed that exploits amplitude modulations (AM) in voice using biologically-inspired models. This system recognizes general dysphonia and four subclasses: hyperfunction, A-P squeezing, paralysis, and vocal fold lesions. The models developed represent processing in the auditory system at the level of the cochlea, auditory nerve, and inferior colliculus. Recognition experiments using dysphonic sentence data obtained from the Kay Elemetrics Disordered Voice Database suggest that our system provides complementary information to state-of-the-art mel-cepstral features. A model for analyzing AM in dysphonic speech is also developed from a traditional communications engineering perspective. Through a case study of seven disordered voices, we show that different AM patterns occur in different frequency bands. This perspective challenges current dysphonia analysis methods that analyze AM in the time-domain signal. by Nicolas Malyska. S.M. 2006-03-24T18:18:43Z 2006-03-24T18:18:43Z 2004 2004 Thesis http://hdl.handle.net/1721.1/30092 55675056 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 117 p. 7694547 bytes 7694355 bytes application/pdf application/pdf application/pdf Massachusetts Institute of Technology |
| spellingShingle | Electrical Engineering and Computer Science. Malyska, Nicolas, 1977- Automatic voice disorder recognition using acoustic amplitude modulation features |
| title | Automatic voice disorder recognition using acoustic amplitude modulation features |
| title_full | Automatic voice disorder recognition using acoustic amplitude modulation features |
| title_fullStr | Automatic voice disorder recognition using acoustic amplitude modulation features |
| title_full_unstemmed | Automatic voice disorder recognition using acoustic amplitude modulation features |
| title_short | Automatic voice disorder recognition using acoustic amplitude modulation features |
| title_sort | automatic voice disorder recognition using acoustic amplitude modulation features |
| topic | Electrical Engineering and Computer Science. |
| url | http://hdl.handle.net/1721.1/30092 |
| work_keys_str_mv | AT malyskanicolas1977 automaticvoicedisorderrecognitionusingacousticamplitudemodulationfeatures |