Isolated-word sentence recognition using probabilistic context-free grammar

A `probabilistic context-free grammar (PCFG)´ is one step up from a Markov model. Any Markov model can be formulated as a PCFG but not the other way round. In this respect it is possible to formulate a grammar model which can´t do any worse than a Markov model in speech recognition. However, this would not normally be the approach to grammar formulation. A grammar is better suited to the structural modelling of language, and grammars for application to speech recognition are likely to be derived from efforts to model the language used in a corpus. There is therefore no guarantee that such grammars will perform better than the bigram or trigram models also derived from that corpus, at least at first. Language modelling for engineering applications (as opposed to linguistics) is still in its infancy, so it would seem that the most important things to do at this stage are to develop the tools and establish the feasibility of the approaches. The paper reports progress in these directions