The performance of speech recgnition systems degrades when the basic sound units used are poorly defined or inconsistently used. Several attempts have been made to improve dictionaries automatically, either by redefining pronunciations of words in terms of existing sound units, or by redefining the sound units themselves completely. The problem with these approaches is that, while the former is limited by the sound units used, the latter discards all human information that has been incorporated into an expert-designed recognition dictionary. In this paper we propose a new merging-and-splitting algorithm that attempts to redefine the basic sound units used in the dictionary, whiel maintaining the expert knowledge built into a manually designed dictionary. Sound units from an existing dictionary are merged based on their inherent confusability, as measured by a Monte-Carlo based metric, and subsequently split to maximize the likelihood of the training data. Experiemtns with the Resource Management database indicate that this approach results in an improvement in recognition accuracy when context-independent models are used for recognition. When context-dependent models are used for recognition. When context-dependent models are used, the improvement observeed is reduced.
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