Relevance based language models
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
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Up-to-date language modeling is recognized to be a critical aspect of maintaining the level of performance for a speech recognizer over time for most applications. In particular for applications such as transcription of broadcast news and conversations where the occurrence of new words is very frequent, especially for highly inflected languages like the European Portuguese. An unsupervised adaptation approach, which dynamically adapts the active vocabulary and language model during a multi-pass speech recognition process, is presented. Experimental results confirmed the adequacy of the proposed approaches. Experiments were carried out for a European Portuguese Broadcast News transcription system with the best preliminary results yielding a relative reduction of 65.2% in OOV word rate and 6.6% in WER.