Novel multiscale methods for nonlinear speech analysis

Abstract : This thesis presents an exploratory research on the application of a nonlinear multiscale formalism, called the Microcanonical Multiscale Formalism (the MMF), to the analysis of speech signals. Derived from principles in Statistical Physics, the MMF allows accurate analysis of the nonlinear dynamics of complex signals. It relies on the estimation of local geometrical parameters, the singularity exponents (SE), which quantify the degree of predictability at each point of the signal domain. When correctly defined and estimated, these exponents can provide valuable information about the local dynamics of complex signals and has been successfully used in many applications ranging from signal representation to inference and prediction.We show the relevance of the MMF to speech analysis and develop several applications to show the strength and potential of the formalism. Using the MMF, in this thesis we introduce: a novel and accurate text-independent phonetic segmentation algorithm, a novel waveform coder, a robust accurate algorithm for detection of the Glottal Closure Instants, a closed-form solution for the problem of sparse linear prediction analysis and finally, an efficient algorithm for estimation of the excitation source signal.
Document type :
Theses
Complete list of metadatas

Cited literature [117 references]  Display  Hide  Download

https://tel.archives-ouvertes.fr/tel-00821896
Contributor : Abes Star <>
Submitted on : Monday, May 13, 2013 - 2:03:04 PM
Last modification on : Thursday, January 11, 2018 - 6:20:16 AM
Long-term archiving on: Monday, August 19, 2013 - 3:50:10 PM

File

Vahid_Khanagha.pdf
Version validated by the jury (STAR)

Identifiers

  • HAL Id : tel-00821896, version 1

Collections

Citation

Vahid Khanagha. Novel multiscale methods for nonlinear speech analysis. Other [cs.OH]. Université Sciences et Technologies - Bordeaux I, 2013. English. ⟨NNT : 2013BOR14737⟩. ⟨tel-00821896⟩

Share

Metrics

Record views

682

Files downloads

646