An acoustic distance measure for automatic cross-language phoneme mapping
Jayren Jugpal Sooful, Elizabeth C. Botha · 2001
This paper explores an automated approach to mapping one phoneme set to another, based on the acoustic distances of the individual phonemes. The main goal of this investigation is to automate the technique for creating initial/baseline acoustic models for a new language. Using this technique, it would be possible to rapidly build speech recognition systems for a variety of languages. A subsidiary objective of this investigation is to compare different acoustic distance measures and to assess their ability to quantify the acoustic similarity between phonemes. The distance measures that were considered for this investigation are the Kullback-Leibler measure, the Bhattacharyya distance metric, the Mahalanobis measure, the Euclidean measure, the L2 metric and the Jeffreys-Matusita distance. Both the TIMIT and SUN Speech corpora were used. It was found that by selecting an appropriate distance measure, an automated procedure to map phonemes from a source language (English) to a target language (Afrikaans) can be applied, with recognition results comparable to a manual mapping process undertaken by a phonetic expert.