Name
Title | ||
|---|---|---|
Towards Fully Bayesian Speaker Recognition: Integrating Out The Between-Speaker Covariance |
Abstract | ||
|---|---|---|
We propose a variational Bayes solution to integrate out the model parameters in a generative i-vector speaker recognizer. The existing state-of-the-art in generative i-vector modelling plugs in fixed maximum-likelihood point-estimates of model parameters. This recipe, may suffer from over-fitting of especially the between-speaker covariance. We show how to integrate out the between-speaker covariance and demonstrate dramatic improvements on NIST SRE 2010. |
Year | Venue | Keywords |
|---|---|---|
2011 | 12TH ANNUAL CONFERENCE OF THE INTERNATIONAL SPEECH COMMUNICATION ASSOCIATION 2011 (INTERSPEECH 2011), VOLS 1-5 | speaker recognition, i-vectors, variational Bayes |
Field | DocType | Citations |
Pattern recognition,Computer science,Speech recognition,Speaker recognition,NIST,Artificial intelligence,Generative grammar,Machine learning,Bayes' theorem,Bayesian probability,Covariance | Conference | 0 |
PageRank | References | Authors |
0.34 | 1 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jesús A. Villalba | 1 | 57 | 4.55 |
| Niko Brümmer | 2 | 595 | 44.01 |