Phone clustering using the Bhattacharyya distance
Brian Kan-Wing Mak, Etienne Barnard · 2002
The authors study the use of the classification-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 classification 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 data-driven approach together with a novel a-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 commonly used broad categories. They 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.