Speech recognition in a unification grammar framework

The authors describe a stochastic unification grammar system that is a generalization of the conventional hidden Markov model (HMM) approach. Unification grammars concisely model context, providing a more powerful characterization of the acoustic data than the first-order Markov process. It is shown that this approach generalizes traditional FSA (finite-state automaton)-based HMM systems and that a stochastic chart parsing algorithm produces the exact same solutions as an existing FSA-based system. The shift from automata to grammars allows efficient processing of complex language models by hypothesizing symbols once per frame, no matter how many times they are needed. As an added benefit, the chart parsing algorithm allows parallel processing of lower level hypotheses autonomously with no fundamental algorithm changes.<>