Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/3749
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dc.coverage.spatialPhotonicsen_US
dc.date.accessioned2012-04-25T06:30:22Z-
dc.date.available2012-04-25T06:30:22Z-
dc.date.issued2012-04-25-
dc.identifier.urihttp://hdl.handle.net/10603/3749-
dc.description.abstractMedical fields require fast, simple and nonmvaSlve methods of diagnostic techniques. Several methods are available and possible because of the growth of technology that provides the necessary means of collecting and processing signals. The present thesis details the work done in the field of voice signals. New methods of analysis have been developed to understand the complexity of voice signals, such as nonlinear dynamics aiming at the exploration of voice signals dynamic nature. The purpose of this thesis is to characterize complexities of pathological voice from healthy signals and to differentiate stuttering signals from healthy signals. Efficiency of various acoustic as well as non linear time series methods are analysed. Three groups of samples are used, one from healthy individuals, subjects with vocal pathologies and stuttering subjects. Individual vowels /(7Jf?)/, /@./, and /~/ and a continuous speech data for the utterance of the sentence "~ro~ruro~o ruQJ1CD> ~6GT3ocmlQ)oroo6T11" the meaning in English is "Both are good friends" from Malayalam language are recorded using a microphone . The recorded audios is converted to digital signals and are subjected to analysis. Acoustic perturbation methods like fundamental frequency (FO), jitter, shimmer, Zero Crossing Rate(ZCR) were carried out and non linear measures like maximum lyapunov exponent«Amax), correlation dimension (D2), Kolmogorov exponent(K2), and a new measure of entropy viz., Permutation entropy (PE) are evaluated for all three groups of the subjects. Permutation Entropy is a nonlinear complexity measure which can efficiently distinguish regular and complex nature of any signal and extract infonnation about the change in dynamics of the process by indicating sudden change in its value. The results shows that nonlinear dynamical methods seem to be a suitable technique for voice signal analysis, due to the chaotic component of the human voice. Permutation entropy is well suited due to its sensitivity to uncertainties, since the pathologies are characterized by an increase in the signal complexity and unpredictability. Pathological groups have higher entropy values compared to the normal group. The stuttering signals have lower entropy values compared to the normal signals. PE is effective m charaterising the level of improvement after two weeks of speech therapy in the case of stuttering subjects. PE is also effective in characterizing the dynamical difference between healthy and pathological subjects. This suggests that PE can improve and complement the recent voice analysis methods available for clinicians. The work establishes the application of the simple, inexpensive and fast algorithm of PE for diagnosis in vocal disorders and stuttering subjects.en_US
dc.format.extentxvi, 157p.en_US
dc.languageEnglishen_US
dc.relation--en_US
dc.rightsuniversityen_US
dc.titleSpeech analysis using modern techniques of nonlinear dynamicsen_US
dc.creator.researcherRadhakrishnan, P Men_US
dc.subject.keywordSpeech analysisen_US
dc.subject.keywordNonlinear dynamicsen_US
dc.description.noteSummary p. 152-155, List of publications p. 156-157, References given in chaptersen_US
dc.contributor.guideNampoori, V P Nen_US
dc.publisher.placeCochinen_US
dc.publisher.universityCochin University of Science and Technologyen_US
dc.publisher.institutionInternational School of Photonicsen_US
dc.date.registeredn.den_US
dc.date.completed10/11/2009en_US
dc.date.awarded2009en_US
dc.format.accompanyingmaterialNoneen_US
dc.type.degreePh.D.en_US
dc.source.inflibnetINFLIBNETen_US
Appears in Departments:International School of Photonics

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01_title.pdfAttached File87.11 kBAdobe PDFView/Open
02_declaration.pdf16.32 kBAdobe PDFView/Open
03_certificate.pdf16.78 kBAdobe PDFView/Open
04_acknowledgements.pdf46.89 kBAdobe PDFView/Open
05_abstract.pdf38.51 kBAdobe PDFView/Open
06_table of contents.pdf26.61 kBAdobe PDFView/Open
07_list of figures & tables.pdf85.32 kBAdobe PDFView/Open
08_abbreviations.pdf16.76 kBAdobe PDFView/Open
09_chapter 1.pdf176.46 kBAdobe PDFView/Open
10_chapter 2.pdf364.68 kBAdobe PDFView/Open
11_chapter 3.pdf1.83 MBAdobe PDFView/Open
12_chapter 4.pdf1.17 MBAdobe PDFView/Open
13_chapter 5.pdf811.98 kBAdobe PDFView/Open
14_chapter 6.pdf64.69 kBAdobe PDFView/Open
15_research publications.pdf29 kBAdobe PDFView/Open


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