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In 1989, our group first reported on the development of SUM- MIT, a segment-based speaker-independent continuous-speech re- cognition system (13) . The initial version of SUMMIT made use of fairly simple context-independent models for the lexical labels. Recently, we have begun to incorporate more complex models of lexical labels that take into account a variety of contextual fac- tors. These changes, along with an improved corrective training procedure for adapting pronunciation arc weights and a larger set of training data, have resulted in the reduction of error rate by almost a factor of two on the Resource Management task.

Modelling context dependency in acoustic-phonetic and lexical representations