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Title: Hierarchical Bayesian Language Models for Conversational Speech Recognition
Authors: Huang, Songfang
Renals, Steve
Issue Date: 2010
Journal Title: IEEE Transactions on Audio, Speech and Language Processing
Volume: 18
Issue: 8
Page Numbers: 1941--1954
Publisher: IEEE
Abstract: Traditional n-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 the Pitman--Yor process. This offers a principled approach to language model smoothing, embedding the power-law distribution for natural language. Experiments on the recognition of conversational speech in multiparty meetings demonstrate that by using hierarchical Bayesian language models, we are able to achieve significant reductions in perplexity and word error rate.
URI: http://dx.doi.org/10.1109/TASL.2010.2040782
http://hdl.handle.net/1842/4528
Appears in Collections:CSTR publications

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