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Massachusetts Institute of Technology

Analysis of nonmodal glottal event patterns with application to automatic speaker recognition

Abstract

dc:description.abstract

Regions of phonation exhibiting nonmodal characteristics are likely to contain information about speaker identity, language, dialect, and vocal-fold health. As a basis for testing such dependencies, we develop a representation of patterns in the relative timing and height of nonmodal glottal pulses. To extract the timing and height of candidate pulses, we investigate a variety of inverse-filtering schemes including maximum-entropy deconvolution that minimizes predictability of a signal and minimum-entropy deconvolution that maximizes pulse-likeness. Hybrid formulations of these methods are also considered. we then derive a theoretical framework for understanding frequency- and time-domain properties of a pulse sequence, a process that sheds light on the transformation of nonmodal pulse trains into useful parameters. In the frequency domain, we introduce the first comprehensive mathematical derivation of the effect of deterministic and stochastic source perturbation on the short-time spectrum. We also propose a pitch representation of nonmodality that provides an alternative viewpoint on the frequency content that does not rely on Fourier bases. In developing time-domain properties, we use projected low-dimensional histograms of feature vectors derived from pulse timing and height parameters. For these features, we have found clusters of distinct pulse patterns, reflecting a wide variety of glottal-pulse phenomena including near-modal phonation, shimmer and jitter, diplophonia and triplophonia, and aperiodicity. Using temporal relationships between successive feature vectors, an algorithm by which to separate these different classes of glottal-pulse characteristics has also been developed.

Degree

thesis:*
Department dc:contributor.department
Harvard University--MIT Division of Health Sciences and Technology.
Grantor dc:publisher
Massachusetts Institute of Technology
Year dc:date.issued
2008

Author and committee

dc:creator, dc:contributor.*
Author dc:creator
  • Malyska, Nicolas, 1977-
Advisor dc:contributor.advisor
  • Thomas F. Quatieri.

Subjects

dc:subject × 1

Rights

dc:rights
Statement dc:rights
  • M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.
Language dc:language.iso
eng

Identifiers

dc:identifier.*
Handle dc:identifier.uri
http://hdl.handle.net/1721.1/43804
OAI identifier oai:identifier
oai:dspace.mit.edu:1721.1/43804

Chain of custody

source
Harvested from
MIT
Base URL
dspace.mit.edu/oai/request
Last updated
2026-07-22
Source record
OAI-PMH GetRecord
citation

Malyska, Nicolas, 1977-. Analysis of nonmodal glottal event patterns with application to automatic speaker recognition. Massachusetts Institute of Technology, 2008. http://hdl.handle.net/1721.1/43804