Applications of broad class knowledge for noise robust speech recognition

[摘要] (cont.) In this thesis, we develop a technique to identify islands from broad classes which have been confidently identified from the input signal. We explore a technique to prune the search space given island/gap knowledge. Finally, to further limit the amount of computation in unreliable regions, we investigate scoring less detailed broad class models in gap regions and more detailed phonetic models in island regions. Experiments on both small and large scale vocabulary tasks indicate that the island-driven search strategy results in an improvement in recognition accuracy and computation time.

[发布机构] Massachusetts Institute of Technology