We present a two stage automatic speech recognition architecture suited for applications, such as spoken document retrieval, where large scale language models can be used and very low out-ofvocabulary rates need to be reached. The proposed system couples a weakly constrained phone-recognizer with a phone-to-word decoder that was originally developed for phrase-based statistical machine translation. The decoder permits to efficiently decode confusion networks in input, and to exploit large scale unpruned language models. Preliminary experiments are reported on the transcription of speeches of the Italian parliament. The use of phone confusion networks as interface between the two decoding steps permits to reduce the WER by 28%, thus making the system perform relatively close to a state-of-the-art baseline using a comparable language model.
Bibliographic reference. Bertoldi, Nicola / Federico, Marcello / Falavigna, Daniele / Gerosa, Matteo (2008): "Fast speech decoding through phone confusion networks", In INTERSPEECH-2008, 2094-2097.