Name
Abstract | ||
|---|---|---|
Recently, several nonparametric Bayesian models have been proposed to automatically discover acoustic units in unlabeled data. Most of them are trained using various versions of the Gibbs Sampling (GS) method. In this work, we consider Variational Bayes (VB) as alternative inference process. Even though VB yields an approximate solution of the posterior distribution it can be easily parallelized which makes it more suitable for large database. Results show that, notwithstanding VB inference is an order of magnitude faster, it outperforms GS in terms of accuracy. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1016/j.procs.2016.04.033 | Procedia Computer Science |
Keywords | Field | DocType |
Bayesian non-parametric,Variational Bayes,acoustic unit discovery | Data mining,Inference,Computer science,Nonparametric bayesian,Posterior probability,Artificial intelligence,Approximate solution,Order of magnitude,Machine learning,Gibbs sampling,Bayes' theorem | Conference |
Volume | ISSN | Citations |
81 | 1877-0509 | 13 |
PageRank | References | Authors |
0.71 | 5 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lucas Ondel | 1 | 35 | 7.16 |
| Lukás Burget | 2 | 296 | 33.15 |
| Jan Cernocký | 3 | 1273 | 135.94 |