Abstract: A method for biometric identification for use with a computing device is provided herein. The method includes capturing a temporal sequence of images of the face of a user at different locations within a three-dimensional interaction space. The method further includes extracting one or more face descriptors from the images and generating a biometric template compiling the face descriptors.

Abstract: A system and method are disclosed for tracking image and audio data over time to automatically identify a person based on a correlation of their voice with their body in a multi-user game or multimedia setting.

Abstract: Embodiments for shader-based finite state machine frame detection for implementing alternative graphical processing on an animation scenario are disclosed. In accordance with one embodiment, the embodiment includes assigning an identifier to each shader used to render animation scenarios. The embodiment also includes defining a finite state machine for a key frame in each of the animation scenarios, whereby each finite state machine representing a plurality of shaders that renders the key frame in each animation scenario. The embodiment further includes deriving a shader ID sequence for each finite state machine based on the identifier assigned to each shader. The embodiment additionally includes comparing an input shader ID sequence of a new frame of a new animation scenario to each derived shader ID sequences. Finally, the embodiment includes executing alternative graphics processing on the new animation scenario when the input shader ID sequence matches one of the derived shader ID sequences.

Abstract: A speech recognition system includes a receiver component that receives a distorted speech utterance. The speech recognition also includes an adaptor component that selectively adapts parameters of a compressed model used to recognize at least a portion of the distorted speech utterance, wherein the adaptor component selectively adapts the parameters of the compressed model based at least in part upon the received distorted speech utterance.

Abstract: Embodiments for single-pass bounding box calculation are disclosed. In accordance with one embodiment, the single-pass bounding box calculation includes rendering a first target to a 2-dimensional screen space, whereby the first target includes at least six pixels. The calculation further includes producing transformed vertices in a set of geometry primitives based on an application-specified transformation. The calculation also includes generating six new points for each transformed vertex in the set of geometry primitives. The calculation additionally includes producing an initial third coordinate value for each pixel by rendering the at least six new points generate for each pixel to each corresponding pixel. The calculation further includes producing a post-rasterization value for each pixel by rasterizing the at least six new points rendered to each pixel with each corresponding pixel.

Abstract: A speech recognition system described herein includes a receiver component that receives a distorted speech utterance. The speech recognition also includes an updater component that is in communication with a first model and a second model, wherein the updater component automatically updates parameters of the second model based at least in part upon joint estimates of additive and convolutive distortions output by the first model, wherein the joint estimates of additive and convolutive distortions are estimates of distortions based on a phase-sensitive model in the speech utterance received by the receiver component. Further, distortions other than additive and convolutive distortions, including other stationary and nonstationary sources, can also be estimated used to update the parameters of the second model.

Abstract: A video game system (or other data processing system) can visually identify a person entering a field of view of the system and determine whether the person has been previously interacting with the system. In one embodiment, the system establishes thresholds, enrolls players, performs the video game (or other application) including interacting with a subset of the players based on the enrolling, determines that a person has become detectable in the field of view of the system, automatically determines whether the person is one of the enrolled players, maps the person to an enrolled player and interacts with the person based on the mapping if it is determined that the person is one of the enrolled players, and assigns a new identification to the person and interacts with the person based on the new identification if it is determined that the person is not one of the enrolled players.

Abstract: Noise and channel distortion parameters in the vectorized logarithmic or the cepstral domain for an utterance may be estimated, and subsequently the distorted speech parameters in the same domain may be updated using an unscented transformation framework during online automatic speech recognition. An utterance, including speech generated from a transmission source for delivery to a receiver, may be received by a computing device. The computing device may execute instructions for applying the unscented transformation framework to speech feature vectors, representative of the speech, in order to estimate, in a sequential or online manner, static noise and channel distortion parameters and dynamic noise distortion parameters in the unscented transformation framework. The static and dynamic parameters for the distorted speech in the utterance may then be updated from clean speech parameters and the noise and channel distortion parameters using non-linear mapping.

Abstract: A method of compensating for additive and convolutive distortions applied to a signal indicative of an utterance is discussed. The method includes receiving a signal and initializing noise mean and channel mean vectors. Gaussian dependent matrix and Hidden Markov Model (HMM) parameters are calculated or updated to account for additive noise from the noise mean vector or convolutive distortion from the channel mean vector. The HMM parameters are adapted by decoding the utterance using the previously calculated HMM parameters and adjusting the Gaussian dependent matrix and the HMM parameters based upon data received during the decoding. The adapted HMM parameters are applied to decode the input utterance and provide a transcription of the utterance.

Abstract: A video game system (or other data processing system) can visually identify a person entering a field of view of the system and determine whether the person has been previously interacting with the system. In one embodiment, the system establishes thresholds, enrolls players, performs the video game (or other application) including interacting with a subset of the players based on the enrolling, determines that a person has become detectable in the field of view of the system, automatically determines whether the person is one of the enrolled players, maps the person to an enrolled player and interacts with the person based on the mapping if it is determined that the person is one of the enrolled players, and assigns a new identification to the person and interacts with the person based on the new identification if it is determined that the person is not one of the enrolled players.

Abstract: A system and method are disclosed for tracking image and audio data over time to automatically identify a person based on a correlation of their voice with their body in a multi-user game or multimedia setting.

Abstract: Described is a calibration model for use in a speech recognition system. The calibration model adjusts the confidence scores output by a speech recognition engine to thereby provide an improved calibrated confidence score for use by an application. The calibration model is one that has been trained for a specific usage scenario, e.g., for that application, based upon a calibration training set obtained from a previous similar/corresponding usage scenario or scenarios. Different calibration models may be used with different usage scenarios, e.g., during different conditions. The calibration model may comprise a maximum entropy classifier with distribution constraints, trained with continuous raw confidence scores and multi-valued word tokens, and/or other distributions and extracted features.

Abstract: Techniques and systems for training an acoustic model are described. In an embodiment, a technique for training an acoustic model includes dividing a corpus of training data that includes transcription errors into N parts, and on each part, decoding an utterance with an incremental acoustic model and an incremental language model to produce a decoded transcription. The technique may further include inserting silence between a pair of words into the decoded transcription and aligning an original transcription corresponding to the utterance with the decoded transcription according to time for each part. The technique may further include selecting a segment from the utterance having at least Q contiguous matching aligned words, and training the incremental acoustic model with the selected segment. The trained incremental acoustic model may then be used on a subsequent part of the training data. Other embodiments are described and claimed.

Abstract: An exemplary method for tessellating a primitive of a graphical object includes receiving information for a primitive of a graphical object where the information includes vertex information and an edge factor for each edge of the primitive; based on the received information, dividing the primitive into parts where each part corresponds to at least a portion of an edge of the primitive and at least one vertex of the primitive and where each part has an association with the edge factor of the corresponding edge; for each of the parts, executing a geometry shader on a graphics processing unit (GPU) where the executing includes determining barycentric coordinates for a respective part based in part on its associated edge factor; for each of the parts, outputting the barycentric coordinates to a vertex buffer; and generating a tessellated mesh for the primitive based on the vertex information and the barycentric coordinates of the vertex buffer where the generating includes invoking a draw function of the GPU.

Abstract: An exemplary method for emulating a graphics processing unit (GPU) includes executing a graphics application on a host computing system to generate commands for a target GPU wherein the host computing system includes host system memory and a different, host GPU; converting the generated commands into intermediate commands; based on one or more generated commands that call for one or more shaders, caching one or more corresponding shaders in a shader cache in the host system memory; based on one or more generated commands that call for one or more resources, caching one or more corresponding resources in a resource cache in the host system memory; based on the intermediate commands, outputting commands for the host GPU; and based on the output commands for the host GPU, rendering graphics using the host GPU where output commands that call for one or more shaders access the one or more corresponding shaders in the shader cache and where output commands that call for one or more resources access the one or more

Abstract: Emulating legacy hardware using IEEE 754 compliant hardware is disclosed herein. In some aspects, the emulation includes locating an instruction that includes NaN (not a number) as at least one of an operand or a resultant. The emulation adjusts the resultant of the instruction, via additional code, to produce a final resultant of non-compliant (legacy) hardware. Legacy software, which was written in anticipation of processing by legacy hardware, may then be processed using compliant hardware.

Abstract: Embodiments for shader-based finite state machine frame detection for implementing alternative graphical processing on an animation scenario are disclosed. In accordance with one embodiment, the embodiment includes assigning an identifier to each shader used to render animation scenarios. The embodiment also includes defining a finite state machine for a key frame in each of the animation scenarios, whereby each finite state machine representing a plurality of shaders that renders the key frame in each animation scenario. The embodiment further includes deriving a shader ID sequence for each finite state machine based on the identifier assigned to each shader. The embodiment additionally includes comparing an input shader ID sequence of a new frame of a new animation scenario to each derived shader ID sequences. Finally, the embodiment includes executing alternative graphics processing on the new animation scenario when the input shader ID sequence matches one of the derived shader ID sequences.

Abstract: Providing content based cache for graphic resource management is disclosed herein. In some aspects, a portion of a shadow copy of graphics resources is updated from an original copy of the graphics resources when a requested resource is not current. The shadow copy may be dedicated to a graphics processing unit (GPU) while the original copy may be maintained by a central processing unit (CPU). In further aspects, the requested graphics resource in the shadow copy may be compared to a corresponding graphics resource in the original copy when the GPU requests the graphics resource. The comparison may be performed by comparing hashes of each graphics resource and/or by comparing at least a portion of the graphics resources.

Abstract: Embodiments for single-pass bounding box calculation are disclosed. In accordance with one embodiment, the single-pass bounding box calculation includes rendering a first target to a 2-dimensional screen space, whereby the first target includes at least six pixels. The calculation further includes producing transformed vertices in a set of geometry primitives based on an application-specified transformation. The calculation also includes generating six new points for each transformed vertex in the set of geometry primitives. The calculation additionally includes producing an initial third coordinate value for each pixel by rendering the at least six new points generate for each pixel to each corresponding pixel. The calculation further includes producing a post-rasterization value for each pixel by rasterizing the at least six new points rendered to each pixel with each corresponding pixel.

Abstract: A speech recognition system includes a receiver component that receives a distorted speech utterance. The speech recognition also includes an adaptor component that selectively adapts parameters of a compressed model used to recognize at least a portion of the distorted speech utterance, wherein the adaptor component selectively adapts the parameters of the compressed model based at least in part upon the received distorted speech utterance.