We present an online discriminative training approach to graphemeto- phoneme (g2p) conversion. We employ a many-to-many alignment between graphemes and phonemes, which overcomes the limitations of widely used one-to-one alignments. The discriminative structure-prediction model incorporates input segmentation, phoneme prediction, and sequence modeling in a unified dynamic programming framework. The learning model is able to capture both local context features in inputs, as well as non-local dependency features in sequence outputs. Experimental results show that our system surpasses the state-of-the-art on several data sets.
Bibliographic reference. Jiampojamarn, Sittichai / Kondrak, Grzegorz (2009): "Online discriminative training for grapheme-to-phoneme conversion", In INTERSPEECH-2009, 1303-1306.