A broad phonetic classifier

It has been shown that broad phonetic sequences partition a large lexicon into small equivalence classes of words sharing the same sequence. While these results illustrate the power of broad phonetic constraints for differentiating words from one another, they do not suggest how to exploit sequential constraints in recognition. This paper presents a method for decoupling sequential phonetic constraints from a lexicon, by representing allowable broad phonetic sequences in terms of n-th order Markov models. A simple frame-based broad phonetic classifier is used to evaluate the effectiveness of these models in recognition. Tests on 300 sentences from 30 male speakers demonstrate that the addition of sequential constraints improves the classifier´s performance.