Articulatory feature-based methods for acoustic and audio-visual speech recognition

We report on investigations, conducted at the 2006 JHU Summer Workshop, of the use of articulatory features in automatic speech recognition. We explore the use of articulatory features for both observation and pronunciation modeling, and for both audio-only and audio-visual speech recognition. In the area of observation modeling, we use the outputs of a set of multilayer perceptron articulatory feature classifiers (1) directly, in an extension of hybrid HMM/ANN models, and (2) as part of the observation vector in a standard Gaussian mixture-based model, an extension of the now popular “tandem ” approach. In the area of pronunciation modeling, we explore models consisting of multiple hidden streams of states, each corresponding to a different articulatory feature and having soft synchrony constraints, for both audio-only and audio-visual speech recognition. Our models are implemented as dynamic Bayesian networks, and ou