Abstract: A computing system such as a game console maintains and updates a biometric profile of a user. In one aspect, biometric data of the user is continuously obtained from a sensor such as an infrared and visible light camera, and used to update the biometric profile using a machine learning process. In another aspect, a user is prompted to confirm his or her identify when multiple users are detected at the same time and/or when the user is detected with a confidence level which is below a threshold. A real-time image of the user being identified can be displayed on a user interface with user images associated with one or more accounts. In another aspect, the biometric profile is managed by a shell on the computing system, where the shell makes the biometric profile available to any of a number of applications on the computing system.

Abstract: A Deep Neural Network (DNN) model used in an Automatic Speech Recognition (ASR) system is restructured. A restructured DNN model may include fewer parameters compared to the original DNN model. The restructured DNN model may include a monophone state output layer in addition to the senone output layer of the original DNN model. Singular value decomposition (SVD) can be applied to one or more weight matrices of the DNN model to reduce the size of the DNN Model. The output layer of the DNN model may be restructured to include monophone states in addition to the senones (tied triphone states) which are included in the original DNN model. When the monophone states are included in the restructured DNN model, the posteriors of monophone states are used to select a small part of senones to be evaluated.

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 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 high-dimensional posterior-based feature with partial distance elimination may be utilized for speech recognition. The log likelihood values of a large number of Gaussians are needed to generate the high-dimensional posterior feature. Gaussians with very small log likelihoods are associated with zero posterior values. Log likelihoods for Gaussians for a speech frame may be evaluated with a partial distance elimination method. If the partial distance of a Gaussian is already too small, the Gaussian will have a zero posterior value. The partial distance may be calculated by sequentially adding individual dimensions in a group of dimensions. The partial distance elimination occurs when less than all of the dimensions in the group are sequentially added.

Abstract: Technologies pertaining to training a deep neural network (DNN) for use in a recognition system are described herein. The DNN is trained using heterogeneous data, the heterogeneous data including narrowband signals and wideband signals. The DNN, subsequent to being trained, receives an input signal that can be either a wideband signal or narrowband signal. The DNN estimates the class posterior probability of the input signal regardless of whether the input signal is the wideband signal or the narrowband signal.

Abstract: Described herein are various technologies pertaining to a multilingual deep neural network (MDNN). The MDNN includes a plurality of hidden layers, wherein values for weight parameters of the plurality of hidden layers are learned during a training phase based upon training data in terms of acoustic raw features for multiple languages. The MDNN further includes softmax layers that are trained for each target language separately, making use of the hidden layer values trained jointly with multiple source languages. The MDNN is adaptable, such that a new softmax layer may be added on top of the existing hidden layers, where the new softmax layer corresponds to a new target language.

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 computing system such as a game console maintains and updates a biometric profile of a user. In one aspect, biometric data of the user is continuously obtained from a sensor such as an infrared and visible light camera, and used to update the biometric profile using a machine learning process. In another aspect, a user is prompted to confirm his or her identify when multiple users are detected at the same time and/or when the user is detected with a confidence level which is below a threshold. A real-time image of the user being identified can be displayed on a user interface with user images associated with one or more accounts. In another aspect, the biometric profile is managed by a shell on the computing system, where the shell makes the biometric profile available to any of a number of applications on the computing system.

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: 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: 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: Systems and methods for face recognition are provided. In one example, a method for face recognition includes receiving a user image and detecting a user luminance of data representing the user's face. An adaptive low pass filter is selected that corresponds to the user luminance of the user's face. The filter is applied to the user image to create a filtered user image. The filtered user image is projected to create a filtered user image representation. A filtered reference image representation that has been filtered with the same low pass filter is selected from a reference image database. The method then determines whether the filtered reference image representation matches the filtered user image representation.

Abstract: Methods are provided for generating a low dimension pose space and using the pose space to estimate one or more head rotation angles of a user head. In one example, training image frames including a test subject head are captured under a plurality of conditions. For each frame an actual head rotation angle about a rotation axis is recorded. In each frame a face image is detected and converted to an LBP feature vector. Using principal component analysis a PCA feature vector is generated. Pose classes related to rotation angles about a rotation axis are defined. The PCA feature vectors are grouped into a pose class that corresponds to the actual rotation angle associated with the PCA feature vector. Linear discriminant analysis is applied to the pose classes to generate the low dimension pose space.

Abstract: Scalar operations for model adaptation or feature enhancement may be utilized for recognizing an utterance during automatic speech recognition in a noisy environment. An utterance including distorted speech generated from a transmission source for delivery to a receiver, may be received by a computer. The distorted speech may be caused by the noisy environment and channel distortion. Computations using scalar operations in the form of an algorithm may then be performed for recognizing the utterance. As a result of performing all of the computations with scalar operations, computational complexity is very small in comparison to matrix and vector operations. Vector Taylor Series with diagonal Jacobian approximation may also be utilized as a distortion-model-based noise robust algorithm with scalar operations.

Abstract: Methods are provided for generating a low dimension pose space and using the pose space to estimate one or more head rotation angles of a user head. In one example, training image frames including a test subject head are captured under a plurality of conditions. For each frame an actual head rotation angle about a rotation axis is recorded. In each frame a face image is detected and converted to an LBP feature vector. Using principal component analysis a PCA feature vector is generated. Pose classes related to rotation angles about a rotation axis are defined. The PCA feature vectors are grouped into a pose class that corresponds to the actual rotation angle associated with the PCA feature vector. Linear discriminant analysis is applied to the pose classes to generate the low dimension pose space.

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: 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: 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.