Rasetshwane, Daniel Motlotle
(2005)
Identification of Transient Speech Using Wavelet Transforms.
Master's Thesis, University of Pittsburgh.
(Unpublished)
Abstract
It is generally believed that abrupt stimulus changes, which in speech may be time-varying frequency edges associated with consonants, transitions between consonants and vowels and transitions within vowels are critical to the perception of speech by humans and for speech recognition by machines. Noise affects speech transitions more than it affects quasi-steady-state speech. I believe that identifying and selectively amplifying speech transitions may enhance the intelligibility of speech in noisy conditions. The purpose of this study is to evaluate the use of wavelet transforms to identify speech transitions. Using wavelet transforms may be computationally efficient and allow for real-time applications. The discrete wavelet transform (DWT), stationary wavelet transform (SWT) and wavelet packets (WP) are evaluated. Wavelet analysis is combined with variable frame rate processing to improve the identification process. Variable frame rate can identify time segments when speech feature vectors are changing rapidly and when they are relatively stationary. Energy profiles for words, which show the energy in each node of a speech signal decomposed using wavelets, are used to identify nodes that include predominately transient information and nodes that include predominately quasi-steady-state information, and these are used to synthesize transient and quasi-steady-state speech components. These speech components are estimates of the tonal and nontonal speech components, which Yoo et al identified using time-varying band-pass filters. Comparison of spectra, a listening test and mean-squared-errors between the transient components synthesized using wavelets and Yoo's nontonal components indicated that wavelet packets identified the best estimates of Yoo's components. An algorithm that incorporates variable frame rate analysis into wavelet packet analysis is proposed. The development of this algorithm involves the processes of choosing a wavelet function and a decomposition level to be used. The algorithm itself has 4 steps: wavelet packet decomposition; classification of terminal nodes; incorporation of variable frame rate processing; synthesis of speech components. Combining wavelet analysis with variable frame rate analysis provides the best estimates of Yoo's speech components.
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Details
| Item Type: |
University of Pittsburgh ETD
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| Status: |
Unpublished |
| Creators/Authors: |
| Creators | Email | Pitt Username | ORCID  |
|---|
| Rasetshwane, Daniel Motlotle | dmrst51@pitt.edu | DMRST51 | |
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| ETD Committee: |
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| Date: |
21 June 2005 |
| Date Type: |
Completion |
| Defense Date: |
4 April 2005 |
| Approval Date: |
21 June 2005 |
| Submission Date: |
14 April 2005 |
| Access Restriction: |
No restriction; Release the ETD for access worldwide immediately. |
| Institution: |
University of Pittsburgh |
| Schools and Programs: |
Swanson School of Engineering > Electrical Engineering |
| Degree: |
MSEE - Master of Science in Electrical Engineering |
| Thesis Type: |
Master's Thesis |
| Refereed: |
Yes |
| Uncontrolled Keywords: |
speech processing; transient speech; variable frame rate; wavelet transform |
| Other ID: |
http://etd.library.pitt.edu/ETD/available/etd-04142005-154057/, etd-04142005-154057 |
| Date Deposited: |
10 Nov 2011 19:37 |
| Last Modified: |
15 Nov 2016 13:40 |
| URI: |
http://d-scholarship.pitt.edu/id/eprint/7145 |
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