Correcting Errors in Speech Recognition with Articulatory Dynamics

We introduce a novel mechanism for incorporating articulatory dynamics into speech recognition with the theory of task dynamics. This system reranks sentencelevel hypotheses by the likelihoods of their hypothetical articulatory realizations which are derived from relationships learned with aligned acoustic/articulatory data. Experiments compare this with two baseline systems, namely an acoustic hidden Markov model and a dynamic Bayes network augmented with discretized representations of the vocal tract. Our system based on task dynamics reduces worderror rates significantly by 10.2% relative to the best baseline models.

• Type: Journal
• Year: 2010
• Where: ACL
• Authors: Frank Rudzicz
• Keywords: ACL 2010, Acoustic Hidden Markov, Computational Linguistics, Hypothetical Articulatory Realizations, Task Dynamics