Abstract: A method generates a behavioral model of a data center when a machine learning algorithm is applied. A team of human modelers that partition the data center into a plurality of connected nodes is analyzed by a behavioral model. The behavioral model of the data center detects an anomaly in a system behavior center by recursively applying the behavioral model to each node and simple component. A compressed metric vector for the node is generated by reducing a dimension of an input metric vector. A root cause of a failure caused is determined by the anomaly and an action is automatically recommended to an operator to resolve a problem caused by the failure. The proactively actions are taken to keep the data center in a normal state based on the behavioral model using the machine learning algorithm.

Abstract: A method generates a behavioral model of a data center when a machine learning algorithm is applied. A team of human modelers that partition the data center into a plurality of connected nodes is analyzed by a behavioral model. The behavioral model of the data center detects an anomaly in a system behavior center by recursively applying the behavioral model to each node and simple component. A compressed metric vector for the node is generated by reducing a dimension of an input metric vector. A root cause of a failure caused is determined by the anomaly and an action is automatically recommended to an operator to resolve a problem caused by the failure. The proactively actions are taken to keep the data center in a normal state based on the behavioral model using the machine learning algorithm.

Abstract: In one embodiment, a method includes obtaining a text representation, and identifying a current topic structure for the text representation. The first topic structure is initially identified as an initial first topic structure. The method also includes identifying at least a first document that has a first document topic structure that is similar to the current first topic structure, refining the current first topic structure based on the first document topic structure, and introducing topic labels in the text representation based on the current first topic structure.

Abstract: A method is provided in one example embodiment and includes extracting sentences from data, which comprises a speech transcript; tokenizing the plurality of sentences to develop for each of the plurality of sentences a sentence vector and at least one feature vector; and performing topic segmentation on the speech transcript using the sentence vectors and feature vectors, the topic segmentation resulting in a listing of segments corresponding to the speech transcript. In certain embodiments, the feature vector may be at least one of a cue word feature vector, a speaker change feature vector, and a scene change feature vector.

Abstract: In one embodiment, the present invention is a method and apparatus for active noise cancellation. In one embodiment, a method for recognizing user speech in an audio signal received by a media system (where the audio signal includes user speech and ambient audio output produced by the media system and/or other devices) includes canceling portions of the audio signal associated with the ambient audio output and applying speech recognition processing to an uncancelled remainder of the audio signal.

Abstract: An apparatus and a concomitant method for recognizing speech in a noisy environment are provided. The present method includes applying a first interpolation weight to a clean speech model to produce a weighted clean speech model, applying a second interpolation weight to a noise model to produce a weighted noise model, and deriving a noisy speech model directly from the weighted clean speech model and the weighted noise model. At least one of the first interpolation weight and the second interpolation weight is computed in a maximum likelihood framework.

Abstract: A method and apparatus are provided for performing speech recognition using observable and meaningful relationships between words within a single utterance and using a structured data source as a source of constraints on the recognition process. Results from a first constrained speech recognition pass can be combined with information about the observable and meaningful word relationships to constrain or simplify subsequent recognition passes. This iterative process greatly reduces the search space required for each recognition pass, making the speech recognition process more efficient, faster and accurate.

Abstract: In one embodiment, the present invention is a method and apparatus for active noise cancellation. In one embodiment, a method for recognizing user speech in an audio signal received by a media system (where the audio signal includes user speech and ambient audio output produced by the media system and/or other devices) includes canceling portions of the audio signal associated with the ambient audio output and applying speech recognition processing to an uncancelled remainder of the audio signal.

Abstract: An apparatus and a concomitant method for speech recognition. In one embodiment, the present method is referred to as a “Dynamic Noise Compensation” (DNC) method where the method estimates the models for noisy speech using models for clean speech and a noise model. Specifically, the model for the noisy speech is estimated by interpolation between the clean speech model and the noise model. This approach reduces computational cycles and does not require large memory capacity.

Abstract: Probabilistic recognition using clusters and simple probability functions provides improved performance by employing a limited number of clusters each using a relatively large number of simple probability functions. The simple probability functions for each of the limited number of state clusters are greater in number than the limited number of state clusters.

Abstract: Probabilistic recognition using clusters and simple probability functions provides improved performance by employing a limited number of clusters each using a relatively large number of simple probability functions.

Abstract: An apparatus and a concomitant method for speech recognition. In one embodiment, the present method is referred to as a “Dynamic Noise Compensation” (DNC) method where the method estimates the models for noisy speech using models for clean speech and a noise model. Specifically, the model for the noisy speech is estimated by interpolation between the clean speech model and the noise model. This approach reduces computational cycles and does not require large memory capacity.