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Traditional -gram language models are widely used in state-of-the-art large vocabulary speech recognition systems. This simple model suffers from some limitations, such as overfitting of maximum-likelihood estimation and the lack of rich contextual knowledge sources. In this paper, we exploit a hierarchical Bayesian interpretation for language modeling, based on a nonparametric prior called Pitman-Yor process. This offers a principled approach to language model smoothing, embedding thedoi:10.1109/tasl.2010.2040782 fatcat:44xd2wj57navvotw6yt3qcwayy