Dynamic language modeling for a daily broadcast news transcription system

When transcribing Broadcast News data in highly inflected languages, the vocabulary growth leads to high out-of-vocabulary rates. To address this problem, we propose a daily and unsupervised adaptation approach which dynamically adapts the active vocabulary and LM to the topic of the current news segment during a multi-pass speech recognition process. Based on texts daily available on the Web, a story-based vocabulary is selected using a morpho-syntatic technique. Using an Information Retrieval engine, relevant documents are extracted from a large corpus to generate a story-based LM. Experiments were carried out for a European Portuguese BN transcription system. Preliminary results yield a relative reduction of 65.2% in OOV and 6.6% in WER.