A Combined Phonetic-Phonological Approach to Estimating Cross-Language Phoneme Similarity in an ASR Environment

This paper presents a fully automated linguistic approach to measuring distance between phonemes across languages. In this approach, a phoneme is represented by a feature matrix where feature categories are fixed, hierarchically related and binary-valued; feature categorization explicitly addresses allophonic variation and feature values are weighted based on their relative prominence derived from lexical frequency measurements. The relative weight of feature values is factored into phonetic distance calculation. Two phonological distances are statistically derived from lexical frequency measurements. The phonetic distance is combined with the phonological distances to produce a single metric that quantifies cross-language phoneme distance. The performances of target-language phoneme HMMs constructed solely with source language HMMs, first selected by the combined phonetic and phonological metric and then by a data-driven, acoustics distance-based method, are compared in context-independent automatic speech recognition (ASR) experiments. Results show that this approach consistently performs equivalently to the acoustics-based approach, confirming its effectiveness in estimating cross-language similarity between phonemes in an ASR environment.