Hidden conditional random fields with M-to-N alignments for grapheme-to-phoneme conversion
Patrick Lehnen, Stefan Hahn, Vlad-Andrei Guta, Hermann Ney · 2012
Conditional Random Fields have been successfully applied to a number of NLP tasks like concept tagging, named entity tagging, or grapheme-to-phoneme conversion.When no alignment between source and target side is provided with the training data, it is challenging to build a CRF system with state-of-the-art performance.In this work, we present an approach incorporating an Mto-N alignment as a hidden variable within a transducerbased implementation of CRFs.Including integrated estimation of transition penalties, it was possible to train a state-of-the-art hidden CRF system in reasonable time for an English grapheme-to-phoneme conversion task without using an external model to provide the alignment.