Pronunciation variants generation using SMT-inspired approaches

Enriching a pronunciation dictionary with phonological variation is a challenging task, not yet solved despite several decades of research, in particular for speech-to-text transcription of real world data where it is important to cover different pronunciation variants. This paper proposes two alternative methods, inspired by machine translation, to derive pronunciation variants from an initial lexicon with limited variations. In the first case, an n-best pronunciation list is extracted directly from a machine translation tool, used as a grapheme-to-phoneme (g2p) converter. The second is a novel method based on a pivot approach, previously used for the paraphrase extraction task, and here applied as a post-processing step to the g2p converter. Some preliminary speech recognition experiments with the automatically generated pronunciation variants are reported using Quaero development data.