Improved decision trees for phonetic modeling

Bahl et al. (see ICASSP-91, vol.1, p.185-188, 1991) employed decision trees to specify the acoustic realization of a phone as a function of its context. By using a computationally cheap Poisson-based evaluation function, they were able to account for a much wider context than previous researchers (five preceding and five following phones). We extend this work in four ways. (1) We employ the Poisson criterion to find quickly the M best questions at a node during tree expansion, then use an HMM-based MLE criterion to make the final choice from these (and for pruning trees). (2) Bahl et al. use stopping criteria to halt the growth of a tree, which is then used for speech recognition. It is preferable to grow an over-large tree and then prune it; we apply the efficient GRD expansion-pruning algorithm of Gelfand et al. (see IEEE Trans. PAMI, vol.13, no.2, p.163-174, 1991) to the phonetic modeling problem. (3) Like Bahl et al., we allow questions about a large number of preceding and following phones. However, a given search algorithm may make some of these questions difficult to answer. In addition to the “yes” and “no” children of each question, we grow a “don´t know” subtree to be used if a question is unanswerable at present. (4) We have experimented with questions based on phonetic features, as well as questions that ask about the presence of specific phones. Our approach permits an arbitrary feature schema to be read in and used in question generation