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1
Advanced Subspace Techniques for Modeling Channel and Session Variability in a Speaker Recognition System
Remus, Jeremiah
In: DTIC (2012)
Abstract:
The robustness of any speaker recognition system is dependent on its capability for managing the variability in the recording environment. A better ability to quantify that variation may lead to the development of improved methods for reducing the non-speaker influences on performance. In this study, subspace decomposition in combination with three pattern classification techniques was investigated to assess its appropriateness for performing speaker recognition on the MultiRoom8 corpus, a data set with several room and microphone conditions. A partial least squares decomposition of the GMM supervector in combination with a nearest neighbor classifier was consistently a top-performer on the 100 experimental setups consider in this study, which may suggest an approach for mitigating the effects of room and microphone variability in a speaker recognition system through projections to a lower-dimensional feature space. ; The original document contains color images.
Keyword:
*SPEECH RECOGNITION
;
CHANNEL VARIABILITY
;
CHANNELS
;
CLASSIFICATION
;
DATA BASES
;
DECOMPOSITION
;
ENVIRONMENTS
;
LEAST SQUARES METHOD
;
MICROPHONES
;
MODELS
;
PATTERN CLASSIFICATIONS
;
PATTERNS
;
RECORDING SYSTEMS
;
SKILLS
;
SPACE(ROOM)
;
SPEAKER IDENTIFICATION
;
VARIATIONS
;
Voice Communications
;
WUAFRLASIDBA01
URL:
http://oai.dtic.mil/oai/oai?&verb=getRecord&metadataPrefix=html&identifier=ADA557785
http://www.dtic.mil/docs/citations/ADA557785
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