Biomechanically informed nonlinear speech signal processing
Linear digital signal processing based around linear, time-invariant systems theory finds substantial application in speech processing. The linear acoustic source-filter theory of speech production provides ready biomechanical justification for using linear techniques. Nonetheless, biomechanical stu...
| Κύριοι συγγραφείς: | , |
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| Άλλοι συγγραφείς: | |
| Μορφή: | Thesis |
| Γλώσσα: | English |
| Έκδοση: |
2007
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| Θέματα: |
| _version_ | 1826278252171231232 |
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| author | Little, M Max A Little |
| author2 | Roberts, S |
| author_facet | Roberts, S Little, M Max A Little |
| author_sort | Little, M |
| collection | OXFORD |
| description | Linear digital signal processing based around linear, time-invariant systems theory finds substantial application in speech processing. The linear acoustic source-filter theory of speech production provides ready biomechanical justification for using linear techniques. Nonetheless, biomechanical studies surveyed in this thesis display significant nonlinearity and non-Gaussinity, casting doubt on the linear model of speech production. In order therefore to test the appropriateness of linear systems assumptions for speech production, surrogate data techniques can be used. This study uncovers systematic flaws in the design and use of exiting surrogate data techniques, and, by making novel improvements, develops a more reliable technique. Collating the largest set of speech signals to-date compatible with this new technique, this study next demonstrates that the linear assumptions are not appropriate for all speech signals. Detailed analysis shows that while vowel production from healthy subjects cannot be explained within the linear assumptions, consonants can. Linear assumptions also fail for most vowel production by pathological subjects with voice disorders. Combining this new empirical evidence with information from biomechanical studies concludes that the most parsimonious model for speech production, explaining all these findings in one unified set of mathematical assumptions, is a stochastic nonlinear, non-Gaussian model, which subsumes both Gaussian linear and deterministic nonlinear models. As a case study, to demonstrate the engineering value of nonlinear signal processing techniques based upon the proposed biomechanically-informed, unified model, the study investigates the biomedical engineering application of disordered voice measurement. A new state space recurrence measure is devised and combined with an existing measure of the fractal scaling properties of stochastic signals. Using a simple pattern classifier these two measures outperform all combinations of linear methods for the detection of voice disorders on a large database of pathological and healthy vowels, making explicit the effectiveness of such biomechanically-informed, nonlinear signal processing techniques. |
| first_indexed | 2024-03-06T23:41:10Z |
| format | Thesis |
| id | oxford-uuid:6f5b84fb-ab0b-42e1-9ac2-5f6acc9c5b80 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-06T23:41:10Z |
| publishDate | 2007 |
| record_format | dspace |
| spelling | oxford-uuid:6f5b84fb-ab0b-42e1-9ac2-5f6acc9c5b802022-03-26T19:30:14ZBiomechanically informed nonlinear speech signal processingThesishttp://purl.org/coar/resource_type/c_db06uuid:6f5b84fb-ab0b-42e1-9ac2-5f6acc9c5b80Biomedical engineeringInformation engineeringPattern recognition (statistics)Mathematical biologyDynamical systems and ergodic theory (mathematics)EnglishOxford University Research Archive - Valet2007Little, MMax A LittleRoberts, SMcSharry, PMoroz, ILinear digital signal processing based around linear, time-invariant systems theory finds substantial application in speech processing. The linear acoustic source-filter theory of speech production provides ready biomechanical justification for using linear techniques. Nonetheless, biomechanical studies surveyed in this thesis display significant nonlinearity and non-Gaussinity, casting doubt on the linear model of speech production. In order therefore to test the appropriateness of linear systems assumptions for speech production, surrogate data techniques can be used. This study uncovers systematic flaws in the design and use of exiting surrogate data techniques, and, by making novel improvements, develops a more reliable technique. Collating the largest set of speech signals to-date compatible with this new technique, this study next demonstrates that the linear assumptions are not appropriate for all speech signals. Detailed analysis shows that while vowel production from healthy subjects cannot be explained within the linear assumptions, consonants can. Linear assumptions also fail for most vowel production by pathological subjects with voice disorders. Combining this new empirical evidence with information from biomechanical studies concludes that the most parsimonious model for speech production, explaining all these findings in one unified set of mathematical assumptions, is a stochastic nonlinear, non-Gaussian model, which subsumes both Gaussian linear and deterministic nonlinear models. As a case study, to demonstrate the engineering value of nonlinear signal processing techniques based upon the proposed biomechanically-informed, unified model, the study investigates the biomedical engineering application of disordered voice measurement. A new state space recurrence measure is devised and combined with an existing measure of the fractal scaling properties of stochastic signals. Using a simple pattern classifier these two measures outperform all combinations of linear methods for the detection of voice disorders on a large database of pathological and healthy vowels, making explicit the effectiveness of such biomechanically-informed, nonlinear signal processing techniques. |
| spellingShingle | Biomedical engineering Information engineering Pattern recognition (statistics) Mathematical biology Dynamical systems and ergodic theory (mathematics) Little, M Max A Little Biomechanically informed nonlinear speech signal processing |
| title | Biomechanically informed nonlinear speech signal processing |
| title_full | Biomechanically informed nonlinear speech signal processing |
| title_fullStr | Biomechanically informed nonlinear speech signal processing |
| title_full_unstemmed | Biomechanically informed nonlinear speech signal processing |
| title_short | Biomechanically informed nonlinear speech signal processing |
| title_sort | biomechanically informed nonlinear speech signal processing |
| topic | Biomedical engineering Information engineering Pattern recognition (statistics) Mathematical biology Dynamical systems and ergodic theory (mathematics) |
| work_keys_str_mv | AT littlem biomechanicallyinformednonlinearspeechsignalprocessing AT maxalittle biomechanicallyinformednonlinearspeechsignalprocessing |