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Robust Speech Recognition: Articulatory Information to Account for Coarticulation

Current state-of-the-art phone-based speech recognition systems fail to model coarticulation properly. Tri-phone and bi-phone based approaches can only model coarticulatory effects due to the immediate neighboring phones. Phonetic studies have shown that coarticulatory effects can exist beyond the regime of di-phone or tri- phone based models and have also claimed that articulatory information can help to properly address those effects. The objective of our current study is to retrieve articulatory information from speech. Our task is twofold. First we will develop a recurrent neural network (RNN) architecture that predicts articulatory information given synthetic acoustic speech (generated from the TAsk Dynamic and Applications Model) and compare it with previously proposed systems. To evaluate such a system for natural speech we require a natural speech database containing articulatory information. With this in mind, the second goal of our research is to propose and realize a methodology to specify articulatory information for a natural speech database, in this case the X-ray microbeam corpus.