Abstract
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.
Advisor
Rebecca Bates
Committee Member
Vincent Winstead
Committee Member
Qun (Vincent) Zhang
Date of Degree
2016
Language
english
Document Type
Thesis
Degree
Master of Science (MS)
Department
Electrical and Computer Engineering and Technology
College
Science, Engineering and Technology
Recommended Citation
Kim, E. (2016). A Wavelet Transform Module for a Speech Recognition Virtual Machine [Master’s thesis, Minnesota State University, Mankato]. Cornerstone: A Collection of Scholarly and Creative Works for Minnesota State University, Mankato. https://cornerstone.lib.mnsu.edu/etds/603/
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
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