標題: Automatic construction of regression class tree for MLLR via model-based hierarchical clustering
作者: Cheng, Shih-Sian
Xu, Yeong-Yuh
Wang, Hsin-Min
Fu, Hsin-Chia
資訊工程學系
Department of Computer Science
關鍵字: speaker adaptation;MLLR;regression class tree
公開日期: 2006
摘要: In this paper, we propose a model-based hierarchical clustering algorithm that automatically builds a regression class tree for the well-known speaker adaptation technique - Maximum Likelihood Linear Regression (MLLR). When building a regression class tree, the mean vectors of the Gaussian components of the model set of a speaker independent CDHMM-based speech recognition system are collected as the input data for clustering. The proposed algorithm comprises two stages. First, the input data (i.e., all the Gaussian mean vectors of the CDHMMs) is iteratively partitioned by a divisive hierarchical clustering strategy, and the Bayesian Information Criterion (BIC) is applied to determine the number of clusters (i.e., the base classes of the regression class tree). Then, the regression class tree is built by iteratively merging these base clusters using an agglomerative hierarchical clustering strategy, which also uses BIC as the merging criterion. We evaluated the proposed regression class tree construction algorithm on a Mandarin Chinese continuous speech recognition task. Compared to the regression class tree implementation in HTK, the proposed algorithm is more effective in building the regression class tree and can determine the number of regression classes automatically.
URI: http://hdl.handle.net/11536/17092
ISBN: 978-3-540-49665-6
ISSN: 0302-9743
期刊: Chinese Spoken Language Processing, Proceedings
Volume: 4274
起始頁: 390
結束頁: 398
Appears in Collections:Conferences Paper