Date of Award

Fall 1998

Document Type

Thesis

Degree Name

Master of Science (MS)

Department

Electrical & Computer Engineering

Program/Concentration

Electrical Engineering

Committee Director

Stephen A. Zahorian

Committee Member

John W. Stockton

Committee Member

Steven Gray

Call Number for Print

Special Collections LD4331.E55 D36

Abstract

In this research, the variability of Discrete Cosine Transform Coefficient (DCTC) features was investigated. Additionally, a new pitch-synchronous processing method was explored to increase the stability of features and to reduce window effects when compared to the regular method. The noise sources that lead to feature variability were analyzed, and different smoothing methods were tested. It was found that longer frames, frequency warping, time smoothing of the log spectrum, and DCS level time smoothing, all help reduce DCTC variability and increase classification performance. The pitch­ synchronous method was implemented with Matlab. Important processing methods, including pitch period estimation, time­ domain resampling, and pitch-dependent scaled frequency range, were investigated. Twenty-four training speakers and eight test speakers were used for the classification tests. The final results showed similar performance as compared to the conventional method, which implies that window effects are a minor factor in speech recognition. However, processing using a pitch-dependent spectrum led to better performance than the conventional pitch-independent method. In addition, the new method appears to reduce DCTC feature variability by a small amount.

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In Copyright. URI: http://rightsstatements.org/vocab/InC/1.0/ This Item is protected by copyright and/or related rights. You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s).

DOI

10.25777/23dg-jd85

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