Cluster adaptive training with factorized decision trees for speech recognition

Abstract

Cluster adaptive training (CAT) is a popular approach to train multiple-cluster HMMs for fast speaker adaptation in speech recognition. Traditionally, a cluster-independent decision tree is shared among all clusters, which could limit the modelling power of multiple-cluster HMMs. In this paper, each cluster is allowed to have its own decision tree. The intersections between the triphones subsets, corresponding to the leaf nodes of each cluster-dependent trees, are used to define a finer state sharing structure. The parameters of these intersections are constructed from the parameters of the leaf nodes of each individual decision tree. This is referred to as CAT with factorized decision trees (FD-CAT). FD-CAT significantly increases the modelling power without introducing additional free parameters. A novel iterative mean cluster update approach and a robust covariance matrix update method with united statistics are proposed to efficiently train FD-CAT. Experiments showed that using multiple decision trees can yield better performance than single decision tree. Furthermore, FD-CAT significantly outperformed traditional CAT system. Index Terms Adaptation, factorized decision trees, cluster adaptive training, eigenvoices