Abstract: A simulation method and system. A computing system receives a first audio and/or video data stream. The first audio and/or video data stream includes data associated with a first person. The computing system monitors the first audio and/or video data stream. The computing system identifies emotional attributes comprised by the first audio and/or video data stream. The computing system generates a second audio and/or video data stream associated with the first audio and/or video data stream. The second audio and/or video data stream includes the data without the emotional attributes. The computing system stores the second audio and/or video data stream.

Abstract: An optimization system and method includes determining a best gradient as a sparse direction in a function having a plurality of parameters. The sparse direction includes a direction that maximizes change of the function. This maximum change of the function is determined by performing an optimization process that gives maximum growth subject to a sparsity regularized constraint. An extended Baum Welch (EBW) method can be used to identify the sparse direction. A best step size is determined along the sparse direction by finding magnitudes of entries of direction that maximizes the function restricted to the sparse direction. A solution is recursively refined for the function optimization using a processor and storage media.

Abstract: A simulation method and system. A computing system receives a first audio and/or video data stream. The first audio and/or video data stream includes data associated with a first person. The computing system monitors the first audio and/or video data stream. The computing system identifies emotional attributes comprised by the first audio and/or video data stream. The computing system generates a second audio and/or video data stream associated with the first audio and/or video data stream. The second audio and/or video data stream includes the data without the emotional attributes. The computing system stores the second audio and/or video data stream.

Abstract: Techniques are disclosed for using phonetic features for speech recognition. For example, a method comprises the steps of obtaining a first dictionary and a training data set associated with a speech recognition system, computing one or more support parameters from the training data set, transforming the first dictionary into a second dictionary, wherein the second dictionary is a function of one or more phonetic labels of the first dictionary, and using the one or more support parameters to select one or more samples from the second dictionary to create a set of one or more exemplar-based class identification features for a pattern recognition task.

Abstract: Techniques are disclosed for generating and using sparse representation features to improve speech recognition performance. In particular, principles of the invention provide sparse representation exemplar-based recognition techniques. For example, a method comprises the following steps. A test vector and a training data set associated with a speech recognition system are obtained. A subset of the training data set is selected. The test vector is mapped with the selected subset of the training data set as a linear combination that is weighted by a sparseness constraint such that a new test feature set is formed wherein the training data set is moved more closely to the test vector subject to the sparseness constraint. An acoustic model is trained on the new test feature set. The acoustic model trained on the new test feature set may be used to decode user speech input to the speech recognition system.

Abstract: A method of automatically processing text data is described. An initial set of data tags is developed that characterize text data in a text database. Higher order entities are determined which are characteristic of patterns in the data tags. Then the text data is automatically tagged based on the higher order entities.

Abstract: Operation of an automated dialog system is described using a source language to conduct a real time human machine dialog process with a human user using a target language. A user query in the target language is received and automatically machine translated into the source language. An automated reply of the dialog process is then delivered to the user in the target language. If the dialog process reaches an initial assistance state, a first human agent using the source language is provided to interact in real time with the user in the target language by machine translation to continue the dialog process. Then if the dialog process reaches a further assistance state, a second human agent using the target language is provided to interact in real time with the user in the target language to continue the dialog process.

Abstract: A multiple audio/video data stream simulation method and system. A computing system receives first audio and/or video data streams. The first audio and/or video data streams include data associated with a first person and a second person. The computing system monitors the first audio and/or video data streams. The computing system identifies emotional attributes comprised by the first audio and/or video data streams. The computing system generates second audio and/or video data streams associated with the first audio and/or video data streams. The second audio and/or video data streams include the first audio and/or video data streams data without the emotional attributes. The computing system stores the second audio and/or video data streams.

Abstract: A method and apparatus are provided for automatically inducing class based shrinkage features. The method includes clustering each word in a set of word groupings of a given type into a respective one of a plurality of classes. The method further includes selecting and extracting a set of class-based shrinkage features from the set of word groupings based on the plurality of classes. The set of class-based shrinkage features is specifically selected for an intended classification application.

Abstract: A method, information processing system, and computer readable article of manufacture model data. A first dataset is received that includes a first set of physical world data. At least one data model associated with the first dataset is generated based on the receiving. A second dataset is received that includes a second set of physical world data. The second dataset is compared to the at least one data model. A probability that the second dataset is modeled by the at least one data model is determined. A determination is made that the probability is above a given threshold. A decision associated with the second dataset based on the at least one data model is generated in response to the probability being above the given threshold. The probability and the decision are stored in memory. The probability and the decision are provided to user via a user interface.

Abstract: A method, information processing system, and computer readable article of manufacture model data. A first dataset is received that includes a first set of physical world data. At least one data model associated with the first dataset is generated based on the receiving. A second dataset is received that includes a second set of physical world data. The second dataset is compared to the at least one data model. A probability that the second dataset is modeled by the at least one data model is determined. A determination is made that the probability is above a given threshold. A decision associated with the second dataset based on the at least one data model is generated in response to the probability being above the given threshold. The probability and the decision are stored in memory. The probability and the decision are provided to user via a user interface.

Abstract: Techniques for converting spoken speech into written speech are provided. The techniques include transcribing input speech via speech recognition, mapping each spoken utterance from input speech into a corresponding formal utterance, and mapping each formal utterance into a stylistically formatted written utterance.

Abstract: A device, method, and computer program for performing language translation. An example embodiment may include a communications module configured to receive device data from a nearby device. A constraint module is configured to limit a translation domain of the language translation based on the device data. Language translation is performed within the limited translation domain.

Abstract: A multiple audio/video data stream simulation method and system. A computing system receives first audio and/or video data streams. The first audio and/or video data streams include data associated with a first person and a second person. The computing system monitors the first audio and/or video data streams. The computing system identifies emotional attributes comprised by the first audio and/or video data streams. The computing system generates second audio and/or video data streams associated with the first audio and/or video data streams. The second audio and/or video data streams include the first audio and/or video data streams data without the emotional attributes. The computing system stores the second audio and/or video data streams.

Abstract: A multiple audio/video data stream simulation method and system. A computing system receives first audio and/or video data streams. The first audio and/or video data streams include data associated with a first person and a second person. The computing system monitors the first audio and/or video data streams. The computing system identifies emotional attributes comprised by the first audio and/or video data streams. The computing system generates second audio and/or video data streams associated with the first audio and/or video data streams. The second audio and/or video data streams include the first audio and/or video data streams data without the emotional attributes. The computing system stores the second audio and/or video data streams.

Abstract: A multiple audio/video data stream simulation method and system. A computing system receives first audio and/or video data streams. The first audio and/or video data streams include data associated with a first person and a second person. The computing system monitors the first audio and/or video data streams. The computing system identifies emotional attributes comprised by the first audio and/or video data streams. The computing system generates second audio and/or video data streams associated with the first audio and/or video data streams. The second audio and/or video data streams include the first audio and/or video data streams data without the emotional attributes. The computing system stores the second audio and/or video data streams.

Abstract: Techniques are disclosed for using phonetic features for speech recognition. For example, a method comprises the steps of obtaining a first dictionary and a training data set associated with a speech recognition system, computing one or more support parameters from the training data set, transforming the first dictionary into a second dictionary, wherein the second dictionary is a function of one or more phonetic labels of the first dictionary, and using the one or more support parameters to select one or more samples from the second dictionary to create a set of one or more exemplar-based class identification features for a pattern recognition task.

Abstract: A simulation method and system. A computing system receives a first audio and/or video data stream. The first audio and/or video data stream includes data associated with a first person. The computing system monitors the first audio and/or video data stream. The computing system identifies emotional attributes comprised by the first audio and/or video data stream. The computing system generates a second audio and/or video data stream associated with the first audio and/or video data stream. The second audio and/or video data stream includes the data without the emotional attributes. The computing system stores the second audio and/or video data stream.

Abstract: A method for improving the performance of a noise cancellation device, the method includes determining whether one or more noise making objects (NMO) are near an audible range of the noise cancellation device and receiving a signal from the one or more NMOs indicative of a kind of noise the one or more NMOs is generating. The method also includes selecting a specific noise cancellation model to reduce an expected noise in response to the received kind of noise the one or more NMOs is generating.

Abstract: A system for regulating the volume and frequency content of audio producing devices, the system includes: one or more noise making objects (NMO) configured with individual sound control devices in electrical communication with a noise management server and one or more audio producing devices configured with individual sound control devices; wherein the sound control devices have electronic logic processing, storage, and communication capabilities; wherein the noise management server utilize the sound control devices to: determine whether the NMO are producing noise in the audible range of one or more audio producing devices; determine a noise characteristic of the one or more NMO; command the one or more NMO to send the noise characteristic to the one or more audio producing devices; and wherein the volume and frequency content of audio produced by the one or more audio producing devices is adjusted in response to the received noise characteristic.