Name
Abstract | ||
|---|---|---|
Modern approaches to speaker recognition (verification) operate in a space of "supervectors" created via concatenation of the mean vectors of a Gaussian mixture model (GMM) adapted from a universal background model (UBM). In this space, a number of approaches to model inter-class separability and nuisance attribute variability have been proposed. We develop a method for modeling the variability associated with each class (speaker) by using partial-least-squares - a latent variable modeling technique, which isolates the most informative subspace for each speaker. The method is tested on NIST SRE 2008 data and provides promising results. The method is shown to be noise-robust and to be able to efficiently learn the subspace corresponding to a speaker on training data consisting of multiple utterances. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/ICASSP.2011.5947548 | ICASSP |
Keywords | Field | DocType |
Gaussian processes,least squares approximations,speaker recognition,GMM,Gaussian mixture model,NIST SRE,interclass separability,latent variable modeling technique,multiple utterances,nuisance attribute variability,partial least squares,partial-least-squares,speaker recognition,speaker verification,universal background model,GMM supervectors,Partial least squares,latent vector,speaker recognition | Subspace topology,Pattern recognition,Computer science,Support vector machine,Latent variable model,Speech recognition,Speaker recognition,Artificial intelligence,Gaussian process,Speaker diarisation,Concatenation,Mixture model | Conference |
ISSN | Citations | PageRank |
1520-6149 | 0 | 0.34 |
References | Authors | |
8 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Balaji Vasan Srinivasan | 1 | 82 | 14.58 |
| Dmitry N. Zotkin | 2 | 171 | 19.06 |
| Ramani Duraiswami | 3 | 1721 | 161.98 |