This work explores the trade-offs between time and frequency information during the feature extraction process of an automatic speech recognition (ASR) system using wavelet transform (WT) features instead of Mel-frequency cepstral coefficients (MFCCs) and the benefits of combining the WTs and the MFCCs as inputs to an ASR system. A virtual machine from the Speech Recognition Virtual Kitchen resource (www.speechkitchen.org) is used as the context for implementing a wavelet signal processing module in a speech recognition system. Contributions include a comparison of MFCCs and WT features on small and large vocabulary tasks, application of combined MFCC and WT features on a noisy environment task, and the implementation of an expanded signal processing module in an existing recognition system. The updated virtual machine, which allows straightforward comparisons of signal processing approaches, is available for research and education purposes.
First Committee Member
Second Committee Member
Qun (Vincent) Zhang
Date of Degree
Master of Science (MS)
Electrical and Computer Engineering and Technology
Science, Engineering and Technology
Kim, Euisung, "A Wavelet Transform Module for a Speech Recognition Virtual Machine" (2016). All Theses, Dissertations, and Other Capstone Projects. 603.
Creative Commons License
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