Bbn Technologies Corporation

Verizon Corporate Services Group Inc.

Other

Ramp Holdings, Inc.

Francis G Kubala

Sean C Colbath

Scott Shepard

Daben Liu

Daniel Kiecza

Dabien Liu

Jayadev Billa

A system improves recognition results. The system receives multimedia data and recognizes the multimedia data based on training data to generate documents. The system receives user augmentation relating to one of the documents or new documents from a user. The system supplements the training data with the user augmentation or new documents and retrains based on the supplemented training data.

Systems and methods for improving recognition results via user-augmentation of a database

Multimedia recognition system comprising a plurality of indexers configured to receive and analyze multimedia data based on training data and user augmentation relating to one or more of a plurality of generated documents

An audio classification system classifies sounds in an audio stream as belonging to one of a relatively small number of classes. The audio classification system includes a signal analysis component [and a decoder [. The decoder [includes a number of models [-] for performing the audio classifications. In one implementation, the possible classifications include: vowels, fricatives, narrowband, wideband, coughing, gender, and silence. The classified audio may be used to enhance speech recognition of the audio stream.

Systems and methods for classifying audio into broad phoneme classes

A unified clustering tree () generates phoneme clusters based on an input sequence of phonemes. The number of possible clusters is significantly less than the number of possible combinations of input phonemes. Nodes () in the unified clustering tree are arranged into levels such that the clustering tree generates clusters for multiple speech recognition models. Models that correspond to higher levels in the unified clustering tree are coarse models relative to more fine-grain models at lower levels of the clustering tree.

Unified clustering tree

A speech recognition system receives an audio signal and detects various features of the audio signal. For example, the system classifies the audio signal into speech and non-speech portions, genders of speakers corresponding to the speech portions, and channel bandwidths used by the speakers. The system detects speaker turns based on changes in the speakers and assigns labels to the speaker turns. The system verifies the genders of the speakers and the channel bandwidths used by the speakers and identifies one or more languages associated with the audio signal. The system recognizes the speech portions of the audio signal based on the various features of the audio signal.

Systems and methods for providing acoustic classification

A system facilitates the searching and retrieval of multimedia data items. The system receives data items from different types of media sources and identifies regions in the data items. The regions include document regions, section regions, and passage regions. Each of the section regions corresponds to one of the document regions and each of the passage regions corresponds to one of the section regions and one of the document regions. The system stores document identifiers that relate to the document regions in separate document records in a document table, section identifiers that relate to the section regions in separate section records in a section table, and passage identifiers that relate to the passage regions in separate passage records in a passage table.

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Boston, MA, United States of America

Francis G Kubala