Paper: Correcting Errors in Speech Recognition with Articulatory Dynamics

ACL ID P10-1007
Title Correcting Errors in Speech Recognition with Articulatory Dynamics
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2010
Authors

We introduce a novel mechanism for incorporating articulatory dynamics into speech recognition with the theory of task dynamics. This system reranks sentence- level 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 hid- den Markov model and a dynamic Bayes network augmented with discretized rep- resentations of the vocal tract. Our sys- tem based on task dynamics reduces word- error rates significantly by 10.2% relative to the best baseline models.