Factor Analysis Modelling for Speaker Verification with Short Utterances
Vogt, Robert J., Lustri, Christopher J., & Sridharan, Sridha (2008) Factor Analysis Modelling for Speaker Verification with Short Utterances. In Odyssey 2008: The Speaker and Language Recognition Workshop, 21-24 January 2008, Stellenbosch, South Africa.
This paper examines combining both relevance MAP and subspace
speaker adaptation processes to train GMM speaker models
for use in speaker verification systems with a particular focus
on short utterance lengths. The subspace speaker adaptation
method involves developing a speaker GMM mean supervector
as the sum of a speaker-independent prior distribution and
a speaker dependent offset constrained to lie within a low-rank
subspace, and has been shown to provide improvements in accuracy
over ordinary relevance MAP when the amount of training
data is limited.
It is shown through testing on NIST SRE data that combining
the two processes provides speaker models which lead
to modest improvements in verification accuracy for limited
data situations, in addition to improving the performance of the
speaker verification system when a larger amount of available
training data is available.
Citation countsare sourced monthly fromand citation databases.
These databases contain citations from different subsets of available publications and different time periods and thus the citation count from each is usually different. Some works are not in either database and no count is displayed. Scopus includes citations from articles published in 1996 onwards, and Web of Science generally from 1980 onwards.
Citations counts from theindexing service can be viewed at the linked Google Scholar™ search.
Full-text downloadsdisplays the total number of times this work’s files (e.g., a PDF) have been downloaded from QUT ePrints as well as the number of downloads in the previous 365 days. The count includes downloads for all files if a work has more than one.
|Item Type:||Conference Paper|
|Subjects:||Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100)|
|Divisions:||Past > QUT Faculties & Divisions > Faculty of Built Environment and Engineering|
Past > Institutes > Information Security Institute
|Copyright Owner:||Copyright 2008 IEEE|
|Copyright Statement:||Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.|
|Deposited On:||25 Feb 2008|
|Last Modified:||29 Feb 2012 23:48|
Repository Staff Only: item control page