Landmark-based speech recognition: report of the 2004 Johns Hopkins summer workshop

Three research prototype speech recognition systems are described, all of which use recently developed methods from artificial intelligence (specifically support vector machines (SVM); dynamic Bayesian networks, and maximum entropy classification) in order to implement, in the form of an ASR, current theories of human speech perception and phonology. All systems begin with a high-D multiframe acoustic-to-distinctive feature transformation, implemented using SVMs trained to detect and classify acoustic phonetic landmarks. Distinctive feature probabilities estimated by the SVMs are then integrated using one of 3 pronunciation models: a dynamic programming algorithm that assumes canonical pronunciation of each word, a dynamic Bayesian network implementation of articulatory phonology, or a discriminative pronunciation model trained using the methods of maximum entropy classification. Log probability scores computed by these models are then combined, using log-linear combination, with other word scores available in the lattice output of a 1st pass recognizer, and the resulting combination score is used to compute a 2nd-pass speech recognition output.