Phone clustering using the bhattacharyya distance

In this paper we study using the classi cation-based Bhattacharyya distance measure to guide biphone clustering. The Bhattacharyya distance is a theoretical distance measure between two Gaussian distributions which is equivalent to an upper bound on the optimal Bayesian classi cation error probability. It also has the desirable properties of being computationally simple and extensible to more Gaussian mixtures. Using the Bhattacharyya distance measure in a datadriven approach together with a novel 2-Level Agglomerative Hierarchical Biphone Clustering algorithm, generalized left/right biphones (BGBs) are derived. A neural-net based phone recognizer trained on the BGBs is found to have better frame-level phone recognition than one trained on generalized biphones (BCGBs) derived from a set of commonlyused broad categories. We further evaluate the new BGBs on an isolated-word recognition task of perplexity 40 and obtain a 16.2% error reduction over the broad-category generalized biphones (BCGBs) and a 41.8% error reduction over the monophones.