Decision tree state tying based on segmental clustering for acoustic modeling

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.