At&t Intellectual Property I, LP

University of Southern California

At+t Corp.

Shrikanth Sambasivan Narayanan

Aaron Edward Rosenberg

Richard Cameron Rose

Sarangarajan Parthasarathy

Sarangarajan Pathasarathy

Bojana Gajic

Panayiotis Georgiou

Ilija Zeljkovic

Anil Ramakrishna

Nikolaos Malandrakis

Karan Singla

Victor Martinez Palacios

Krishna Somandepalli

Roberto Pieraccini

Esther Levin

Alexandros Potamianos

Dawn L Dutton

Dagen Wang

Abhinav Sethy

Murtaza Bulut

A computer implemented method for analyzing media content includes a step of providing a plurality of narrative files formatted in human readable format. Each narrative file includes a script and/or dialogues tagged with character names along with auxiliary information. Each script includes a plurality of portrayals performed by an associated actor or character. Linguistic representations of content of the narrative files in both abstract and semantic forms is determined. The linguistic representations are connected to higher order representations and mental states. The linguistic representations are connected to behavior and action. Interplay between language constructs and demographics of content creators is analyzed. Content representations towards individuals/groups are adapted to reflect heterogeneity in preferences.

Linguistic analysis of differences in portrayal of movie characters

Speech recognition models are dynamically re-configurable based on user information, application information, background information such as background noise and transducer information such as transducer response characteristics to provide users with alternate input modes to keyboard text entry. Word recognition lattices are generated for each data field of an application and dynamically concatenated into a single word recognition lattice. A language model is applied to the concatenated word recognition lattice to determine the relationships between the word recognition lattices and repeated until the generated word recognition lattices are acceptable or differ from a predetermined value only by a threshold amount. These techniques of dynamic re-configurable speech recognition provide for deployment of speech recognition on small devices such as mobile phones and personal digital assistants as well environments such as office, home or vehicle while maintaining the accuracy of the speech recognition.

System and method of performing user-specific automatic speech recognition

A translation between a source language and a target language is disclosed. The source language items are divided, with primary and secondary source language items or named entities being identified, where the primary and secondary source languages being different from each other and from the target language. The entities in the second source language are translated in a different way. For example, they may be copied into the target language, or translated in a special way that is based on their meaning, e.g, into a term that has a more descriptive meaning in the target language.

Communication system using mixed translating while in multilingual communication

An automatic speech recognition (ASR) system and method is provided for controlling the recognition of speech utterances generated by an end user operating a communications device. The ASR system and method can be used with a mobile device that is used in a communications network. The ASR system can be used for ASR of speech utterances input into a mobile device, to perform compensating techniques using at least one characteristic and for updating an ASR speech recognizer associated with the ASR system by determined and using a background noise value and a distortion value that is based on the features of the mobile device. The ASR system can be used to augment a limited data input capability of a mobile device, for example, caused by limited input devices physically located on the mobile device.

System and method for processing speech recognition

An emotion recognition system for assessing human emotional behavior from communication by a speaker includes a processing system configured to receive signals representative of the verbal and/or non-verbal communication. The processing system derives signal features from the received signals. The processing system is further configured to implement at least one intermediate mapping between the signal features and one or more elements of an emotional ontology in order to perform an emotion recognition decision. The emotional ontology provides a gradient representation of the human emotional behavior.

Emotion recognition system

A socially-cognizant translation system that takes social state between speaker and listener into account when making the translation. The translation may be more formal or less formal based on the social relationship between speaker and listener. Profanities in the source speech may be detected, and used to determine the social cognizance. Also, the source speech can be translated without the profanity in the target language, instead using the meaning of the profanity.

Socially cognizant translation by detecting and transforming elements of politeness and respect

Spoken translation system which detects both speech from the information and also detects meta information streams from the information. A first aspect produces an enriched training corpus of information for use in the machine translation. A second aspect uses two different extraction techniques, and combines them by lattice rescoring.

Spoken translation system using meta information strings

An automatic speech recognition (ASR) system and method is provided for controlling the recognition of speech utterances generated by an end user operating a communications device. The ASR system and method can be used with a communications device that is used in a communications network. The ASR system can be used for ASR of speech utterances input into a mobile device, to perform compensating techniques using at least one characteristic and for updating an ASR speech recognizer associated with the ASR system by determined and using a background noise value and a distortion value that is based on the features of the mobile device. The ASR system can be used to augment a limited data input capability of a mobile device, for example, caused by limited input devices physically located on the mobile device.

System and method for providing a compensated speech recognition model for speech recognition

Forming and/or improving a language model based on data from a large collection of documents, such as web data. The collection of documents is queried using queries that are formed from the language model. The language model is subsequently improved using the information thus obtained. The improvement is used to improve the query. As data is received from the collection of documents, it is compared to a rejection model, that models what rejected documents typically look like. Any document that meets the test is then rejected. The documents that remain are characterized to determine whether they add information to the language model, whether they are relevant, and whether they should be independently rejected. Rejected documents are used to update the rejection model; accepted documents are used to update the language model. Each iteration improves the language model, and the documents may be analyzed again using the improved language model.

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Riverside, CT, United States of America

Shrikanth Sambasivan Narayanan