In this paper, a fast segmental clustering approach to decision tree tying based acoustic modeling is proposed for large vocabulary speech recognition. It is based on a two level clustering scheme for robust decision tree state clustering. This approach extends the conventional segmental K-means approach to phonetic decision tree state tying based acoustic modeling. It achieves high recognition performances while reducing the model training time from days to hours comparing to the approaches based on Baum-Welch training. Experimental results on standard Resource Management and Wall Street Journal tasks are presented which demonstrate the robustness and efficacy of this approach.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.