Paper Info
Title
Discriminative Acoustic Language Recognition Via Channel-Compensated Gmm Statistics
Abstract
We propose a novel design for acoustic feature-based automatic spoken language recognizers. Our design is inspired by recent advances in text-independent speaker recognition, where intra-class variability is modeled by factor analysis in Gaussian mixture model (GMM) space. We use approximations to GMM-likelihoods which allow variable-length data sequences to be represented as statistics of fixed size. Our experiments on NIST LRE'07 show that variability-compensation of these statistics can reduce error-rates by a factor of three. Finally, we show that further improvements are possible with discriminative logistic regression training.
Year
Venue
Keywords
2009
INTERSPEECH 2009: 10TH ANNUAL CONFERENCE OF THE INTERNATIONAL SPEECH COMMUNICATION ASSOCIATION 2009, VOLS 1-5
acoustic language recognition, intersession variability compensation, discriminative training
Field
DocType
Citations 
Computer science,Speaker recognition,Language recognition,Artificial intelligence,Logistic regression,Discriminative model,Spoken language,Pattern recognition,Communication channel,Speech recognition,NIST,Statistics,Mixture model
Conference
14
PageRank 
References 
Authors
1.32
14
6
Name
Order
Citations
PageRank
Niko Brümmer159544.01
Albert Strasheim2262.23
Valiantsina Hubeika310110.05
Petr Schwarz499169.47
Lukas Burget558174.84
Ondřej Glembek685264.75