test_moissonnage_selune
type: Agent

Novel multiscale methods for nonlinear speech analysis
type: Dataset
contact point: Organization
  fn: CCSD
keyword: non-linear-speech-analysis
keyword: phonetic-segmentation
keyword: analyse-non-lineaire-de-la-parole
keyword: analyse-multi-echelles-de-la-parole
keyword: theses
keyword: infoinfo-ohcomputer-science-csother-csoh
keyword: infoeu-reposemanticsdoctoralthesis
keyword: formalisme-microcanonique-multi-echelles
keyword: codage-de-la-parole
keyword: exposants-de-singularite
keyword: glottal-closure-instant-detection
keyword: microcanonical-multiscale-formalism
keyword: segmentation-phonetique
keyword: multi-scale-speech-analysis
keyword: singularity-exponents
keyword: speech-coding
keyword: sparse-linear-prediction-analysis
keyword: detection-des-instants-de-fermeture-glottale
keyword: analyse-par-prediction-lineaire-parcimonieuse
landing page: https://theses.hal.science/tel-00821896
description: 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.
identifier: NNT: 2013BOR14737
issued: 2026-05-11T04:08:49.897098
language: en
modified: 2026-05-11T04:08:49.897104

Novel multiscale methods for nonlinear speech analysis
type: Distribution
access URL: https://theses.hal.science/tel-00821896
format: HTML
issued: 2026-05-11T04:08:49.920929
modified: 2026-05-11T04:08:49.862545