| Paper: | SLP-P20.1 | ||
| Session: | Acoustic Modeling and Adaptation | ||
| Time: | Friday, May 19, 14:00 - 16:00 | ||
| Presentation: | Poster | ||
| Topic: | Speech and Spoken Language Processing: Clustering and novel modeling algorithms | ||
| Title: | HMM State Clustering based on Efficient Cross-Validation | ||
| Authors: | Takahiro Shinozaki, University of Washington, United States | ||
| Abstract: | Decision tree state clustering is explored using a cross validation likelihood criterion. Cross-validation likelihood is more reliable than conventional likelihood and can be efficiently computed using sufficient statistics. It results in a better tying structure and provides a termination criterion that does not rely on empirical thresholds. Large vocabulary recognition experiments on conversational telephone speech show that, for large numbers of tied states, the cross-validation method gives more robust results. | ||
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