| Paper: | SLP-P17.5 | ||
| Session: | Spoken Language Modeling, Identification and Characterization | ||
| Time: | Thursday, May 18, 16:30 - 18:30 | ||
| Presentation: | Poster | ||
| Topic: | Speech and Spoken Language Processing: Language modeling and Adaptation | ||
| Title: | BAYESIAN LEARNING OF N-GRAM STATISTICAL LANGUAGE MODELING | ||
| Authors: | Shuanhu Bai, Haizhou Li, Institute for Infocomm Research, Singapore | ||
| Abstract: | The n-gram language model adaptation is typically formulated using deleted interpolation under the maximum likelihood estimation framework. This paper proposes a Bayesian learning framework for n-gram statistical language model training and adaptation. By introducing a Dirichlet conjugate prior to the n-gram parameters, we formulate the deleted interpolation under maximum a posterior criterion with a Bayesian learning procedure. We study the Bayesian learning formulation for n-gram and continuous n-gram language models. The experiments on North American News Text corpus have validated the effectiveness of the proposed algorithms. | ||
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