Date of Award

4-2018

Degree Type

Thesis

Degree Name

Engineering (M.S.E.)

Department

School of Engineering

First Advisor

Nicholas Baine

Second Advisor

Samhita Rhodes

Third Advisor

Robert Bossemeyer

Academic Year

2017/2018

Abstract

Speech recognition is a very useful technology because of its potential to develop applications, which are suitable for various needs of users. This research is an attempt to enhance the performance of a speech recognition system by combining the visual features (lip movement) with audio features. The results were calculated using utterances of numerals collected from participants inclusive of both male and female genders. Discrete Cosine Transform (DCT) coefficients were used for computing visual features and Mel Frequency Cepstral Coefficients (MFCC) were used for computing audio features. The classification was then carried out using Support Vector Machine (SVM). The results obtained from the combined/fused system were compared with the recognition rates of two standalone systems (Audio only and visual only).

Available for download on Saturday, May 25, 2019

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