AT&T Intellectual Property I, LP

At+t Corp.

Harris Drucker

David Crawford Gibbon

Zhu Liu

Behzad Shahraray

Lee Begeja

Bernard S Renger

Mazin G Rahim

Allen Louis Gorin

Patrick Guy Haffner

Steven Hart Lewis

Richard Vandervoort Cox

Andrea Basso

A system that incorporates teachings of the present disclosure may include, for example, a server having a controller to monitor for media content from a first source that is distributable where the media content is adjusted for distribution to a plurality of communication devices that are associated with a user and a portion of the plurality of communication devices use different communication protocols from another portion of the plurality of communication devices, obtain content metadata from the media content, receive a request for information associated with the media content from a first communication device of the plurality of communication devices of the user, compile the information from the content metadata based at least in part on a user profile associated with the user, and transmit the information to the first communication device. Other embodiments are disclosed.

Apparatus and method for managing media content

Systems and methods herein use an annotation guide to label utterances and speech data with a call type. A system practicing the method embodiment monitors labelers of speech data by presenting via a processor a test utterance to a labeler, receiving input from the labeler that selects a particular call type from a list of call types and determining via the processor if the labeler labeled the test utterance correctly. Based on the determining step, the system revises the annotation guide, retrains the labeler, and/or alters the test utterance.

Systems and methods for monitoring speech data labelers

A system and method is provided for rapidly generating a new spoken dialog application. In one embodiment, a user experience person labels the transcribed data (e.g., 3000 utterances) using a set of interactive tools. The labeled data is then stored in a processed data database. During the labeling process, the user experience person not only groups utterances in various call type categories, but also flags (e.g., 100-200) specific utterances as positive and negative examples for use in an annotation guide. The labeled data in the processed data database can also be used to generate an initial natural language understanding (NLU) model.

System and method for automatic generation of a natural language understanding model

Systems and methods for using an annotation guide to label utterances and speech data with a call type. A method embodiment monitors labelers of speech data by presenting via a processor a test utterance to a labeler, receiving input from the labeler that selects a particular call type from a list of call types and determining via the processor if the labeler labeled the test utterance correctly. Based on the determining step, the method performs at least one of the following: revising the annotation guide, retraining the labeler or altering the test utterance.

Systems and methods for generating an annotation guide. Speech data is organized and presented to a user. After the user selects some of the utterances in the speech data, the selected utterances are included in a class and/or call type. Additional utterances that belong to the class and/or call type can be found in the speech data using relevance feedback, data mining, data clustering, support vector machines, and the like. After a call type is complete, it is committed to the annotation guide. After all call types are completed, the annotation guide is generated.

Systems and methods for generating an annotation guide

Systems and methods for monitoring labelers of speech data. To test or train labelers, a labeler is presented with utterances that have already been identified as belonging to a particular class or call type. The labeler is asked to assign a call type to the utterances. The performance of the labeler is measured by comparing the call types assigned by the labeler with the existing call types of the utterances. The performance of a labeler can also be monitored as the labeler labels speech data by occasionally having the labeler label an utterance that is already labeled and by storing the results.

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Long Branch, NJ, United States of America

Harris Drucker