Speech recognition based on acoustically derived segment units

This paper describes a new method of word model generation based on acoustically derived segment units (henceforth ASUs). An ASU-based approach has the advantages of growing out of human pre-determined phonemes and of consistently generating acoustic units by using the maximum likelihood (ML) criterion. The former advantage is e ective when it is di cult to map acoustics to a phone such as with highly co-articulated spontaneous speech. In order to implement an ASU-based modeling approach in a speech recognition system, we must rst solve two points: (1) How do we design an inventory of acoustically-derived segmental units and (2) How do we model the pronunciations of lexical entries in terms of the ASUs. As for the second question, we propose an ASU-based word model generation method by composing the ASU statistics, that is, their means, variances and durations. The e ectiveness of the proposed method is shown through spontaneous word recognition experiments.