Key points are not available for this paper at this time.
The recognition of broadcast news is a challenging problem in speech recognition. To achieve the long-term goal of robust, real-time news transcription, several problems have to be overcome, e.g. the variety of acoustic conditions and the unlimited vocabulary. In this paper we address the problem of unlimited vocabulary. We show, that this problem is more serious for German than it is for English. Using a speech recognition system with a large vocabulary, we dynamically adapt the active vocabulary to the topic of the current news segment. This is done by using information retrieval (IR) techniques on a large collection of texts automatically gathered from the internet. The same technique is also used to adapt the language model of the recognition system. The process of vocabulary adaptation and language model retraining is completely unsupervised. We show, that dynamic vocabulary adaptation can significantly reduce the out-of-vocabulary (OOV) rate and improve the word error rate of our...
Kemp et al. (Mon,) studied this question.
Synapse has enriched one closely related paper. Consider it for comparative context: