Name
Title | ||
|---|---|---|
State Divergence-Based Determination of The Number of Gaussian Components of Each State in HMM |
Abstract | ||
|---|---|---|
A new, state divergence-based algorithm is proposed in this paper to determine the number of Gaussian components of each state in continuous density HMM by maximizing the between-state divergence. The unscented transform based approximation of the Kullback-Leibler divergence is adopted to measure the between-state model divergence to direct the determination. Due to the advantage of being more discriminative, the proposed approach can lead to more compact HMM. Our experimental evaluation shows that compared with the conventional Bayesian information criterion based determination (which is better than the uniform determination), the presented method can reduce the total number of Gaussian components to about 63%, while it results in almost negligible degradation of the recognition performance |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1109/ICASSP.2006.1660232 | ICASSP (1) |
Keywords | Field | DocType |
speech recognition,kullback-leibler divergence,approximation theory,hmm,gussian components,unscented transform based approximation,state divergence-based determination,gaussian processes,transforms,hidden markov models,information science,parameter estimation,degradation,kullback leibler divergence,length measurement,bayesian information criterion,bayesian methods | Bayesian information criterion,Divergence,Pattern recognition,Unscented transform,Gaussian,Artificial intelligence,Gaussian process,Estimation theory,Hidden Markov model,Mathematics,Kullback–Leibler divergence | Conference |
Volume | Issue | ISSN |
1 | null | 1520-6149 |
ISBN | Citations | PageRank |
1-4244-0469-X | 1 | 0.37 |
References | Authors | |
6 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiao-Bing Li | 1 | 4 | 1.13 |
| Ren-Hua Wang | 2 | 344 | 41.36 |