8 search results - page 1 / 2 » Parameter domain pruning for improving convergence of synthe... |
ISCAS
2005
IEEE
15 years 5 months ago
2005
IEEE
— This paper presents a parameter domain pruning method. Parameter domain pruning aims to identify parameter sub-domains that are more likely to produce feasible and good design ...
ICPR
2008
IEEE
16 years 1 months ago
2008
IEEE
When a large amount of data are missing, or when multiple hidden nodes exist, learning parameters in Bayesian networks (BNs) becomes extremely difficult. This paper presents a lea...
89
Voted
ICASSP
2011
IEEE
14 years 3 months ago
2011
IEEE
The speech parameter generation algorithm considering global variance (GV) for HMM-based speech synthesis proved to be effective against the over-smoothing problem. However, the c...
111
Voted
ICASSP
2011
IEEE
14 years 3 months ago
2011
IEEE
In this work a new adaptive fast variational sparse Bayesian learning (V-SBL) algorithm is proposed that is a variational counterpart of the fast marginal likelihood maximization ...
102
Voted
ICML
2005
IEEE
16 years 29 days ago
2005
IEEE
Unsupervised learning methods often involve summarizing the data using a small number of parameters. In certain domains, only a small subset of the available data is relevant for ...