Category-based phoneme-to-grapheme transliteration

Abstract

Grapheme-based speech recognition systems are faster to develop but typically do not reach the same level of performance as phoneme-based systems. In this paper we introduce a technique for improving the performance of standard grapheme-based systems. We find that by handling a relatively small number of irregular words through phoneme-to-grapheme (P2G) transliteration – transforming the original orthography of irregular words to an ‘idealised’ orthography – grapheme-based accuracy can be improved. An analysis of speech recognition accuracy based on word categories shows that P2G transliteration succeeds in improving certain word categories in which grapheme-based systems typically perform poorly, and that the problematic categories can be identified prior to system development. We evaluate when category-based P2G transliteration is beneficial and discuss how the technique can be implemented in practice.