A Fully Automated Derivation of State-Based Eigentriphones for Triphone Modeling with No Tied States Using Regularization

Recently we proposed an alternative method called eigentriphone to solve the data insufficiency problem in triphone acoustic modeling without the need of state tying. The idea is to treat the acoustic modeling problem of infrequent triphones (“poor triphones”) as an adaptation problem from the more frequent triphones (“rich triphones”): firstly, an eigenbasis is developed over the rich triphones that have sufficient training data and the eigenvectors are called eigentriphones; then the poor triphones are adapted in a fashion similar to eigenvoice adaptation. Since, in general, no states are tied in our method, all triphones (states) are distinct so that they can be more discriminative than tied-state triphones. In our previous work, the number of eigentriphones was determined in advance with a set of development data. In this paper, we investigate simply using all of them with the help of regularization to naturally penalize the less important ones. In addition, the modelbased eigenbasis is replaced by three state-based eigenbases. Experimental evaluation on the WSJ 5K task shows that triphone models trained using our new eigentriphone approach without state tying perform at least as well as the common tied-state triphone models.