Date of Award

Summer 2007

Degree Type

Thesis - Restricted

Degree Name

Master of Science (MS)

Department

Electrical and Computer Engineering

First Advisor

Johnson, Michael T.

Second Advisor

Povinelli, Richard J.

Third Advisor

Struble, Craig A.

Abstract

This research focuses on designing the SPEech Feature Toolbox (SPEFT), a toolbox which integrates a large number of speech features into one graphic interface in the MATLAB environment. The toolbox is designed with a Graphical User Interface (GUI) interface which makes it easy to operate; it also provides batch process capability. Available features are categorized into sub-groups including spectral features, pitch frequency detection, formant detection, pitch related features and other time domain features. A speaking style classification experiment is carried out to demonstrate the use of the SPEFT toolbox, and validate the usefulness of non-traditional features in classifying different speaking styles. The pitch-related features jitter and shimmer are combined with the traditional spectral and energy features MFCC and log energy. A Hidden Markov Models (HMMs) classifier is applied to these combined feature vectors, and the classification results between different feature combinations are compared. A thorough test of the SPEFT toolbox is also presented by comparing the extracted feature results between SPEFT and previous toolboxes across a validation test set.

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