Letter-to-phoneme conversion by inference of rewriting rules

Phonetization is a crucial step for oral document processing. In this paper, a new letter-to-phoneme conversion approach is proposed; it is automatic, simple, portable and efficient. It relies on a machine learning technique initially developed for transliteration and translation; the system infers rewriting rules from examples of words with their phonetic representations. This approach is evaluated in the framework of the Pronalsyl Pascal challenge, which includes several datasets on different languages. The obtained results equal or outperform those of the best known systems. Moreover, thanks to the simplicity of our technique, the inference time of our approach is much lower than those of the best performing state-of-the-art systems.