Name
Title | ||
|---|---|---|
Phoneme Classification using Constrained Variational Gaussian Process Dynamical System. |
Abstract | ||
|---|---|---|
This paper describes a new acoustic model based on variational Gaussian process dynamical system (VGPDS) for phoneme classification. The proposed model overcomes the limitations of the classical HMM in modeling the real speech data, by adopting a nonlinear and nonparametric model. In our model, the GP prior on the dynamics function enables representing the complex dynamic structure of speech, while the GP prior on the emission function successfully models the global dependency over the observations. Additionally, we introduce variance constraint to the original VGPDS for mitigating sparse approximation error of the kernel matrix. The effectiveness of the proposed model is demonstrated with extensive experimental results including parameter estimation, classification performance on the synthetic and benchmark datasets. |
Year | Venue | Field |
|---|---|---|
2012 | NIPS | Kernel (linear algebra),Nonlinear system,Pattern recognition,Computer science,Sparse approximation,Artificial intelligence,Gaussian process,Estimation theory,Hidden Markov model,Machine learning,Dynamical system,Acoustic model |
DocType | Citations | PageRank |
Conference | 9 | 0.62 |
References | Authors | |
7 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Park, Hyunsin | 1 | 11 | 2.69 |
| Sungrack Yun | 2 | 40 | 7.27 |
| Sanghyuk Park | 3 | 65 | 6.71 |
| Kim, Jongmin | 4 | 27 | 2.00 |
| Chang D. Yoo | 5 | 375 | 45.88 |