Abstract: A method indexes and retrieves documents stored in a database. A document feature vector is extracted from each document and the documents are then indexed according to the feature vectors. A spoken query is converted to an intermediate representation representing likelihoods of possible sequential combinations of terms in the spoken query. A query certainty vector is generated from the intermediate representation. Other information is acquired. The other information is combined with the query certainty vector. The query vector and the other information are then compared to each of the document feature vectors to retrieve a ranked result set of documents.

Abstract: A system and method indexes and retrieves documents stored in a database. A document feature vector is extracted for each document to be indexed. The feature vector is projected to a low dimension document feature vector, and the documents are indexed according to the low dimension document feature vectors. A spoken query is represented as a lattice indicating possible sequential combinations of words in the spoken query. The lattice is converted to a query certainty vector, which is also projected to a low dimension query certainty vector. The low dimension query vector is compared to each of the low dimension document feature vectors to retrieve a matching result set of documents.

Abstract: A method selects recordings stored in a database. A spoken query is represented as a phonetic lattice and paths through the phonetic lattice are converted to a set of text queries. The database is searched to generate a playlist of recordings matching the set of text queries and samples of the recordings on the playlist are then played. A particular sample is selected as an acoustic query for searching the database to update the playlist with recording matching the acoustic query. Samples of the recordings on the updated playlist are played and a particular sample of the updated play list is selected. A particular record associated with the sample is then played.

Abstract: A method for constructing a dynamic vocabulary for a speech recognizer used with a database of indexed documents. Key words are first extracted from each of the documents in the database as the documents are indexed. The extracted key words are than used to dynamically construct the vocabulary of the speech recognizer or a search engine.

Abstract: A system and method indexes and retrieves documents stored in a database. A document feature vector is extracted for each document to be indexed. The feature vector is projected to a low dimension document feature vector, and the documents are indexed according to the low dimension document feature vectors. A spoken query is represented as a lattice indicating possible sequential combinations of words in the spoken query. The lattice is converted to a query certainty vector, which is also projected to a low dimension query certainty vector. The low dimension query vector is compared to each of the low dimension document feature vectors to retrieve a matching result set of documents.