Assessment of Severe Apnoea through Voice Analysis, Automatic Speech, and Speaker Recognition Techniques
This study is part of an ongoing collaborative effort between the medical and the signal processing communities to promote research on applying standard Automatic Speech Recognition (ASR) techniques for the automatic diagnosis of patients with severe obstructive sleep apnoea (OSA). Early detection o...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
SpringerOpen
2009-01-01
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| Series: | EURASIP Journal on Advances in Signal Processing |
| Online Access: | http://dx.doi.org/10.1155/2009/982531 |
| _version_ | 1818495683896803328 |
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| author | Rubén Fernández Pozo Jose Luis Blanco Murillo Luis Hernández Gómez Eduardo López Gonzalo José Alcázar Ramírez Doroteo T. Toledano |
| author_facet | Rubén Fernández Pozo Jose Luis Blanco Murillo Luis Hernández Gómez Eduardo López Gonzalo José Alcázar Ramírez Doroteo T. Toledano |
| author_sort | Rubén Fernández Pozo |
| collection | DOAJ |
| description | This study is part of an ongoing collaborative effort between the medical and the signal processing communities to promote research on applying standard Automatic Speech Recognition (ASR) techniques for the automatic diagnosis of patients with severe obstructive sleep apnoea (OSA). Early detection of severe apnoea cases is important so that patients can receive early treatment. Effective ASR-based detection could dramatically cut medical testing time. Working with a carefully designed speech database of healthy and apnoea subjects, we describe an acoustic search for distinctive apnoea voice characteristics. We also study abnormal nasalization in OSA patients by modelling vowels in nasal and nonnasal phonetic contexts using Gaussian Mixture Model (GMM) pattern recognition on speech spectra. Finally, we present experimental findings regarding the discriminative power of GMMs applied to severe apnoea detection. We have achieved an 81% correct classification rate, which is very promising and underpins the interest in this line of inquiry. |
| first_indexed | 2024-12-10T18:23:56Z |
| format | Article |
| id | doaj.art-a70e3180cc4b414482dcd5cca1215a5e |
| institution | Directory Open Access Journal |
| issn | 1687-6172 1687-6180 |
| language | English |
| last_indexed | 2024-12-10T18:23:56Z |
| publishDate | 2009-01-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | EURASIP Journal on Advances in Signal Processing |
| spelling | doaj.art-a70e3180cc4b414482dcd5cca1215a5e2022-12-22T01:38:08ZengSpringerOpenEURASIP Journal on Advances in Signal Processing1687-61721687-61802009-01-01200910.1155/2009/982531Assessment of Severe Apnoea through Voice Analysis, Automatic Speech, and Speaker Recognition TechniquesRubén Fernández PozoJose Luis Blanco MurilloLuis Hernández GómezEduardo López GonzaloJosé Alcázar RamírezDoroteo T. ToledanoThis study is part of an ongoing collaborative effort between the medical and the signal processing communities to promote research on applying standard Automatic Speech Recognition (ASR) techniques for the automatic diagnosis of patients with severe obstructive sleep apnoea (OSA). Early detection of severe apnoea cases is important so that patients can receive early treatment. Effective ASR-based detection could dramatically cut medical testing time. Working with a carefully designed speech database of healthy and apnoea subjects, we describe an acoustic search for distinctive apnoea voice characteristics. We also study abnormal nasalization in OSA patients by modelling vowels in nasal and nonnasal phonetic contexts using Gaussian Mixture Model (GMM) pattern recognition on speech spectra. Finally, we present experimental findings regarding the discriminative power of GMMs applied to severe apnoea detection. We have achieved an 81% correct classification rate, which is very promising and underpins the interest in this line of inquiry.http://dx.doi.org/10.1155/2009/982531 |
| spellingShingle | Rubén Fernández Pozo Jose Luis Blanco Murillo Luis Hernández Gómez Eduardo López Gonzalo José Alcázar Ramírez Doroteo T. Toledano Assessment of Severe Apnoea through Voice Analysis, Automatic Speech, and Speaker Recognition Techniques EURASIP Journal on Advances in Signal Processing |
| title | Assessment of Severe Apnoea through Voice Analysis, Automatic Speech, and Speaker Recognition Techniques |
| title_full | Assessment of Severe Apnoea through Voice Analysis, Automatic Speech, and Speaker Recognition Techniques |
| title_fullStr | Assessment of Severe Apnoea through Voice Analysis, Automatic Speech, and Speaker Recognition Techniques |
| title_full_unstemmed | Assessment of Severe Apnoea through Voice Analysis, Automatic Speech, and Speaker Recognition Techniques |
| title_short | Assessment of Severe Apnoea through Voice Analysis, Automatic Speech, and Speaker Recognition Techniques |
| title_sort | assessment of severe apnoea through voice analysis automatic speech and speaker recognition techniques |
| url | http://dx.doi.org/10.1155/2009/982531 |
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