Abstract
Treeβbased clustering is an effective method for sharing the state of an HMM in which clustering is applied to a set of contextβdependent models with the phoneme context as the splitting condition. In past papers, the method has been restricted to the single Gaussian HMM. The single Gaussian HMM, however, is insufficient for representing the acoustic features, and an adequate topology (sharing of HMM state) will not necessarily be realized. Furthermore, in order to arrive at a stateβsharing model with the desired number of mixtures, the process of doubling the number of mixtures and the embedded training must be iterated after the treeβbased clustering, which increases the time for training. Consequently, this paper proposes a method in which the treeβbased clustering algorithm for the single Gaussian HMM is extended to the clustering of the mixed Gaussian HMM. The proposed method reduces the training time to approximately oneβthird that of the conventional method of handling the single Gaussian HMM. A recognition experiment using a phone typewriter and a recognition experiment for continuous word demonstrate that the recognition rate is improved by one to two points. Β© 2002 Wiley Periodicals, Inc. Syst Comp Jpn, 33(4): 40β49, 2002; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/scj.1118