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Lexical access experiments with context-dependent articulatory feature-based models

By Preethi Jyothi, Karen Livescu and Eric Fosler-lussier


We address the problem of pronunciation variation in conversational speech with a context-dependent articulatory featurebased model. The model is an extension of previous work using dynamic Bayesian networks, which allow for easy factorization of a state into multiple variables representing the articulatory features. We build context-dependent decision trees for the articulatory feature distributions, which are incorporated into the dynamic Bayesian networks, and experiment with different sets of context variables. We evaluate our models on a lexical access task using a phonetically transcribed subset of the Switchboard corpus. We find that our models outperform a context-dependent phonetic baseline. Index Terms — Lexical access, articulatory features, dynamic Bayesian networks 1

Year: 2011
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