Adaptive vocabularies for transcribing multilingual broadcast news

One of the most prevailing problems of large-vocabulary speech recognition systems is the large number of out-of-vocabulary words. This is especially the case for automatically transcribing broadcast news in languages other than English, that have a large number of inflections and compound words. We introduce a set of techniques to decrease the number of out-of-vocabulary words during recognition by using linguistic knowledge about morphology and a two-pass recognition approach, where the first pass only serves to dynamically adapt the recognition dictionary to the speech segment to be recognized. A second recognition run is then carried out on the adapted vocabulary. With the proposed techniques we were able to reduce the OOV-rate by more than 40% thereby also improving recognition results by an absolute 5.8% from 64% word accuracy to 69.8%.