Island-driven search using broad phonetic classes

Most speech recognizers do not differentiate between reliable and unreliable portions of the speech signal during search. As a result, most of the search effort is concentrated in unreliable areas. Island-driven search addresses this problem by first identifying reliable islands and directing the search out from these islands towards unreliable gaps. In this paper, we develop a technique to detect islands from knowledge of hypothesized broad phonetic classes (BPCs). Using this island/gap knowledge, we explore a method to prune the search space to limit computational effort in unreliable areas. In addition, we also investigate scoring less detailed BPC models in gap regions and more detailed phonetic models in islands. Experiments on both small and large scale vocabulary tasks indicate that our island-driven search strategy results in an improvement in recognition accuracy and computation time.