Using information retrieval methods for language model adaptation

In this paper we report experiments on language model adaptation using information retrieval methods, drawing upon recent developments in information extraction and topic tracking. One of the problems is extracting reliable topic information with high confidence from the audio signal in the presence of recognition errors. The work in the information retrieval domain on information extraction and topic tracking suggested a new way to solve this problem. In this work, we make use of information retrieval methods to extract topic information in the word recognizer hypotheses, which are then used to automatically select adaptation data from a very large general text corpus. Two adaptive language models, a mixture based model and a MAP based model, have been investigated using the adaptation data. Experiments carried out with the LIMSI Mandarin broadcast news transcription system gives a relative character error rate reduction of 4.3% with this adaptation method.