4th International Conference on Spoken Language Processing

Philadelphia, PA, USA
October 3-6, 1996

Phone Clustering using the Bhattacharyya Distance

Brian Mak, Etienne Barnard

Center for Spoken Language Understanding, Oregon Graduate Institute of Science and Technology, Portland, OR, USA

In this paper we study using 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 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 commonly-used 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.

Full Paper

Bibliographic reference.  Mak, Brian / Barnard, Etienne (1996): "Phone clustering using the bhattacharyya distance", In ICSLP-1996, 2005-2008.