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 M-to-N alignment as a hidden variable within a transducer-based 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.
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