CLUSTERING OF BOOTSTRAPPED ACOUSTIC MODEL WITH FULL COVARIANCE
Presented by: Xin Chen, Author(s): Xin Chen, University of Missouri, United States; Xiaodong Cui, Jian Xue, Peder Olsen, IBM, United States; John Hersey, Mitsubishi, United States; Bowen Zhou, IBM, United States; Yunxin Zhao, University of Missouri, United States
HMM-based acoustic models built from bootstrap are generally very large, especially when full covariance matrices are used for Gaussians. Therefore, clustering is needed to compact the acoustic model to a reasonable size for practical applications. This paper discusses and investigates multiple distance measurements and algorithms for the clustering. The distance measurements include Entropy, KL, Bhattacharyya, Chernoff and their weighted versions. For clustering algorithms, besides conventional greedy bottom-up, algorithms such as N-Best distance Refinement (NBR), K-step Look-Ahead (KLA), Breadth-First Searched (BFS) best path are proposed. A two-pass optimization approach is also proposed to improve the model structure. Experiments in the Bootstrap and Restructuring (BSRS) framework on Pashto show that the discussed clustering approach can lead to better quality of the restructured model. It also shows that final acoustic model that is diagonalized from the full covariance yields good improvement over BSRS model directly with diagonal model and yields significant improvement over the conventional diagonal model.