Microsoft Technology Licensing, LLC

Trausti Thor Kristjansson

John Randall Hershey

Hagai Thomas Attias

Andrew David Wilson

Steven Mark Drucker

Kentaro Toyama

Paul A Viola

Cormac E Herley

Nebojsa Jojic

Kumar Hemachandra Chellapilla

Tim Paek

Maneesh Agrawala

Sumit Basu

Ronald Keith Logan

A method and apparatus identify a clean speech signal from a noisy speech signal. The noisy speech signal is converted into frequency values in the frequency domain. The parameters of at least one posterior probability of at least one component of a clean signal value are then determined based on the frequency values. This determination is made without applying a frequency-based filter to the frequency values. The parameters of the posterior probability distribution are then used to estimate a set of frequency values for the clean speech signal. A clean speech signal is then constructed from the estimated set of frequency values.

Method and apparatus for high resolution speech reconstruction

A method and apparatus are provided for learning a model for the appearance of an object while tracking the position of the object in three dimensions. Under embodiments of the present invention, this is achieved by combining a particle filtering technique for tracking the object's position with an expectation-maximization technique for learning the appearance of the object. Two stereo cameras are used to generate data for the learning and tracking.

Method and apparatus for scene learning and three-dimensional tracking using stereo video cameras

A system and process for controlling common information displays, referred to as shared displays, is presented. The system and process allows multiple modes of input using a set of modules that accept and display data from a variety of sources. Input modules are able to understand data from a single mode of communication and to be able to generate messages as output accordingly. An optional translation module takes discrete message units and converts them into commands or requests that can be processed by a logic module. The logic module includes any application that is running on the shared display. A layout module lays out the information output by the logic module and a display module takes the layout data and converts the information to a form that can be readily displayed on a display device.

System and process for controlling a shared display given inputs from multiple users using multiple input modalities

Systems and methods are described that facilitate assisting a user in filling forms. The invention can parse untagged media, such as email, etc., to identify elements that can be employed to populate a form. Identified elements can be automatically inserted into corresponding fields in a form, and status indicators can be associated therewith and indicated to a user in order to alert the user that review of a particular field in the form is warranted. Additionally, the untagged media and the form can be concurrently presented to the user to facilitate user supervision and/or interaction during assisted form filling.

Assisted form filling

A computer-implemented method and apparatus are provided for populating an electronic form from an electronic image. The method and apparatus identify a size, orientation and position of an object within the electronic image, and identify information elements from pixels within the image that correspond to the object. Fields of the electronic form are displayed to a user along with the identified information elements through a graphical user interface. The information elements are parsed into tagged groups of different information types. At least some of the fields of the electronic form are populated with the tagged groups to produce a populated form. The user is allowed to edit the populated fields through the graphical user interface.

Methods and apparatus for populating electronic forms from scanned documents

A system and method facilitating speech detection and/or enhancement utilizing audio/video fusion is provided. The present invention fuses audio and video in a probabilistic generative model that implements cross-model, self-supervised learning, enabling rapid adaptation to audio visual data. The system can learn to detect and enhance speech in noise given only a short (e.g., 30 second) sequence of audio-visual data. In addition, it automatically learns to track the lips as they move around in the video.

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Redmond, WA, United States of America

Trausti Thor Kristjansson