The first pass produces a rough transcription and then unsupervised model adaptation is used to generate more accurate models for each cluster of acoustic segments. These adapted models are then used with a 4-gram language model to generate the final output. The system is trained on about 150 hours of acoustic training data and 260 million words of broadcast news and newspaper transcriptions. On the Internet audio used here the run-time is increased due to reduced audio quality but the general level of transcription accuracy remains high. Instead they are used to suggest additional words that can be added to the original query.
Relevance feedback is also available.
This allows the user to mark the documents that contain relevant information and have the system suggest additional query words that distinguish those from the non-relevant documents. This can be done in two ways. The retrieval engine is run on these long-term queries only on login. If the retriever finds a document that matches one of these queries and which has been added to the data base since the user's last session, the query is highlighted. This feature effectively allows the user to filter the incoming broadcasts.