Eigentriphones: A basis for context-dependent acoustic modeling

In context-dependent acoustic modeling, it is important to strike a balance between detailed modeling and data sufficiency for robust estimation of model parameters. In the past, parameter sharing or tying is one of the most common techniques to solve the problem. In recent years, another technique which may be loosely and collectively called the subspace approach tries to express a phonetic or sub-phonetic unit in terms of a small set of canonical vectors or units. In this paper, we investigate the development of an eigenbasis over the triphones and model each triphone as a point in the basis. We call the eigenvectors in the basis eigentriphones. From another perspective, we investigate the use of the eigenvoice adaptation method as a general acoustic modeling method for training triphones - especially the less frequent triphones without tying their states so that all the triphones are really distinct from each other and thus may be more discriminative. Experimental evaluation on the 5K-vocabulary HUB2 recognition task shows that a triphone HMM system trained using only eigentriphones without state tying may achieve slightly better performance than the common tied-state triphones.