Laura, Manuel
(2006)
The Application of Blind Source Separation to Feature Decorrelation and Normalizations.
Master's Thesis, University of Pittsburgh.
(Unpublished)
Abstract
We apply a Blind Source Separation BSS algorithm to the decorrelation of Mel-warped cepstra. The observed cepstra are modeled as a convolutive mixture of independent source cepstra. The algorithm aims to minimize a cross-spectral correlation at different lags to reconstruct the source cepstra. Results show that using "independent" cepstra as features leads to a reduction in the WER.Finally, we present three different enhancements to the BSS algorithm. We also present some results of these deviations of the original algorithm.
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Details
| Item Type: |
University of Pittsburgh ETD
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| Status: |
Unpublished |
| Creators/Authors: |
|
| ETD Committee: |
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| Date: |
3 October 2006 |
| Date Type: |
Completion |
| Defense Date: |
11 April 2005 |
| Approval Date: |
3 October 2006 |
| Submission Date: |
20 March 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: |
cepstrum; speech features; BSS; LaTeX |
| Other ID: |
http://etd.library.pitt.edu/ETD/available/etd-03202005-171259/, etd-03202005-171259 |
| Date Deposited: |
10 Nov 2011 19:32 |
| Last Modified: |
15 Nov 2016 13:37 |
| URI: |
http://d-scholarship.pitt.edu/id/eprint/6532 |
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