Fuzzy parameter clustering method in speech recognition
The paper proposes a parameter clustering method using fuzzy clustering analysis to decrease the parameter size efficiently and improve the robustness of parameter training in large vocabulary continuous speech recognition systems. Based on the structure of a phonetic decision tree, leaf nodes are used for Gaussian fuzzy clustering and the root node or shallow leaf nodes are used for covariance fuzzy sharing. Experimental results show that, when the number of Gaussians is reduced by 50%, recognition accuracy only decreases by 0.55% compared to the baseline. By combining covariance fuzzy sharing, a significant performance increasing is achieved over the conventional system with approximately the same parameter size.