Current systems for speech recognition suffer from uncertainty: rather than delivering a uniquely-identified word, each input segment is associated with a set of recognition candidates or word-hypotheses. Thus an input sequence of sounds or images leads to, not an unambiguous sequence of words, but a lattice of word-hypotheses. To choose the best candidate from each word-hypothesis set (i.e. to find the best route through the lattice) , linguistic context needs to be taken into account, at several levels: lexis and morphology, parts-of-speech, phrase structure, semantics and pragmatics. We believe that an intuitively simple, naive model will suffice at each level; the sophistication required for full Natural Language Understanding (NLU) (e.g. Alvey Natural Language Toolkit (ANLT)) is inappropriate for real-time language recognition. We describe here models of each linguistic level which are simple but robust and computationally straightforward (hence `pragmatic' in the everyday sense) and which have clear theoretical shortcomings in the eyes of linguistic purists but which nevertheless do the job
