Refining tree-based state clustering by means of formal concept analysis, balanced decision trees and automatically generated model-sets

Decision tree-based state clustering has emerged in as the most popular approach for clustering the states of context dependent hidden Markov model based speech recognizers. The application of sets of phones, mainly phonetically motivated, that limit the possible clusters, results in a reasonably good modeling of unseen phones while it still enables to model specific phones very precisely whenever this is necessary and enough training data is available. Formal concept analysis, a young mathematical discipline, provides means for the treatment of sets and sets of sets that are well suited for further improving tree-based state clustering. The possible refinements are outlined and evaluated in this paper. The major merit is the proposal of procedures for the adaptation of the number of sets used for clustering to the amount of available training data, and of a method that generates suitable sets automatically without the incorporation of additional knowledge