| Paper: | SP-P12.3 | ||
| Session: | Acoustic Modeling: Model Complexity, General Topics | ||
| Time: | Thursday, May 20, 09:30 - 11:30 | ||
| Presentation: | Poster | ||
| Topic: | Speech Processing: Acoustic Modeling for Speech Recognition | ||
| Title: | AUTOMATIC GENERATION OF NON-UNIFORM HMM STRUCTURES BASED ON VARIATIONAL BAYESIAN APPROACH | ||
| Authors: | Takatoshi Jitsuhiro; ATR, Spoken Language Translation Laboratories | ||
| Satoshi Nakamura; ATR, Spoken Language Translation Laboratories | |||
| Abstract: | We propose using the Variational Bayesian (VB) approach for automatically creating non-uniform, context-dependent HMM topologies. The Maximum Likelihood (ML) criterion is generally used to create HMM topologies. However, it has an over-fitting problem. Information criteria have been used to overcome this problem, but theoretically they cannot be applied to complicated models like HMMs. Recently, to avoid these problems, the VB approach has been developed in the machine-learning field. We introduce the VB approach to the Successive State Splitting (SSS) algorithm, which can create both contextual and temporal variations for HMMs. We define the prior and posterior probability densities and free energy with latent variables as split and stop criteria. Experimental results show that the proposed method can automatically create a more efficient model and obtain better performance, especially for vowels, than the original method. | ||
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