Iterative filtering of phonetic transcriptions of proper nouns

This paper focuses on an approach to enhancing automatic phonetic transcription of proper nouns by using an iterative filter to retain only the most relevant part of a large set of phonetic variants, obtained by combining rule-based generation with extraction from actual audio signals. Using this technique, we were able to reduce the error rate affecting proper nouns during automatic speech transcription of the ESTER corpus of French broadcast news. The role of the filtering was to ensure that the new phonetic variants of proper nouns would not induce new errors in the transcription of the rest of the words.