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The Application of Blind Source Separation to Feature Decorrelation and Normalizations

Laura, Manuel (2006) The Application of Blind Source Separation to Feature Decorrelation and Normalizations. Master's Thesis, University of Pittsburgh. (Unpublished)

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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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Item Type: University of Pittsburgh ETD
Status: Unpublished
CreatorsEmailPitt UsernameORCID
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Committee ChairEl-Jaroudi, Amroamro@ee.pitt.eduAMRO
Committee MemberChaparro, Luis F.chaparro@ee.pitt.eduLFCH
Committee MemberLoughlin, Patrickloughlin@engr.pitt.eduLOUGHLIN
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:, etd-03202005-171259
Date Deposited: 10 Nov 2011 19:32
Last Modified: 15 Nov 2016 13:37


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