Author Type

Graduate Student

Date of Award

2009

Document Type

Thesis

Publication Status

Version of Record

Submission Date

October 2026

Abstract

This work explores the process of model-based classification of speech audio signals using low-level feature vectors. The process of extracting low-level features from audio signals is described along with a discussion of established techniques for training and testing mixture model-based classifiers and using these models in conjunction with feature selection algorithms to select optimal feature subsets. The results of a number of classification experiments using a publicly available speech database, the Berlin Database of Emotional Speech, are presented. This includes experiments in optimizing feature extraction parameters and comparing different feature selection results from over 700 candidate feature vectors for the tasks of classifying speaker gender, identity, and emotion. In the experiments, final classification accuracies of 99.5%, 98.0% and 79% were achieved for the gender, identity and emotion tasks respectively.

Extent

xiv, 186 p. : ill.

Comments

by Chris Thoman.|Thesis (M.S.C.S.)--Florida Atlantic University, 2009.|Includes bibliography.|Electronic reproduction. Boca Raton, Fla., 2009. Mode of access: World Wide Web.|by Chris Thoman.

Identifier

FADT210518

Format

pdf

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