Unsupervised bootstrapping of diphone-like templates for connected speech recognition

This paper describes an unsupervised procedure for the construction of template sets for connected speech recognition. The procedure has been developed for use in a speech recognition system based on a "segment spotting" approach, where the segments are diphone-like units. The procedure makes use of both phonetic and acoustic knowledge: the former consists of a model of all the words in the task language in terms of the chosen units; the latter is implicitly represented by an initial set of "training" templates. The performance obtained by using the bootstrapped templates in a connected digit recognition task is good (average word error rate of less than 4%).