Phone Classification in Pseudo-Euclidean Vector Spaces

Abstract

Recently we have proposed a structural framework for modelling speech, which is based on patterns of phonological distinctive features, a linguistically well-motivated alternative to standard vector-space acoustic models like HMMs. This framework gives considerable representational freedom by working with features that have explicit linguistic interpretation, but at the expense of the ability to apply the wide range of analytical decision algorithms available in vector spaces, restricting oneself to more computationally expensive and less-developed symbolic metric tools. In this paper we show that a dissimilarity-based distance-preserving transition from the original structural representation to a corresponding pseudo-Euclidean vector space is possible. Promising results of phone classification experiments conducted on the TIMIT database are reported.