Name
Abstract | ||
|---|---|---|
In total variability modeling, variable length speech utterances are mapped to fixed low-dimensional i-vectors. Central to computing the total variability matrix and i-vector extraction, is the computation of the posterior distribution for a latent variable conditioned on an observed feature sequence of an utterance. In both cases the prior for the latent variable is assumed to be non-informative,... |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/TASLP.2016.2515506 | IEEE/ACM Transactions on Audio, Speech, and Language Processing |
Keywords | Field | DocType |
Covariance matrices,Training,Speech,Microphones,Speech processing,Computational modeling,Speaker recognition | Speech processing,Computer science,Latent variable,Posterior probability,Speaker recognition,Artificial intelligence,Sparse matrix,Pattern recognition,Expectation–maximization algorithm,Speech recognition,NIST,Prior probability,Statistics | Journal |
Volume | Issue | ISSN |
24 | 3 | 2329-9290 |
Citations | PageRank | References |
5 | 0.41 | 18 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sven Ewan Shepstone | 1 | 18 | 3.69 |
| Kong-Aik Lee | 2 | 709 | 60.64 |
| Haizhou Li | 3 | 3678 | 334.61 |
| Zheng-Hua Tan | 4 | 457 | 60.32 |
| Søren Holdt Jensen | 5 | 1362 | 111.79 |