Sony Interactive Entertainment Inc.

Lakshmish Kaushik

Zhenhao Ge

Saket Kumar

Masanori Omote

Xiaoyu Liu

Sharath Rao

A method for integration of real-world content into a game is described. The method includes receiving a request to play the game and accessing overlay multimodal data generated from a portion of real-world multimodal data received as user generated content (RGC). The overlay multimodal data relates to authored multimodal data generated for the game. The method includes replacing the authored multimodal data in one or more scenes of the game with the overlay multimodal data.

Systems and methods for integrating real-world content in a game

Techniques are described for training neural networks on variable length datasets. The numeric representation of the length of each training sample is randomly perturbed to yield a pseudo-length, and the samples sorted by pseudo-length to achieve lower zero padding rate (ZPR) than completely randomized batching (thus saving computation time) yet higher randomness than strictly sorted batching (thus achieving better model performance than strictly sorted batching).

Semi-sorted batching with variable length input for efficient training

A wake-up word for a digital assistant may be specified by a user to trigger the digital assistant to respond to the wake-up word, with the user providing one or more initial pronunciations of the wake-up word. The wake-up word may be unique, or at least not determined beforehand by a device manufacturer or developer of the digital assistant. The initial pronunciation(s) of the keyword may then be augmented with other potential pronunciations of the wake-up word that might be provided in the future, and those other potential pronunciations may then be pruned down to a threshold number of other potential pronunciations. One or more recordings of the initial pronunciation(s) of the wake-up may then be used to train a phoneme recognizer model to better recognize future instances of the wake-up word being spoken by the user or another person using the initial pronunciation or other potential pronunciations.

Phoneme recognizer customizable keyword spotting system with keyword adaptation

Text and speech from a computer simulation are processed by a machine learning engine to animate the face of a computer avatar.

Facial animation control by automatic generation of facial action units using text and speech

An inexperienced computer simulation player is assisted in playing the simulation by identifying in previously-played simulations successful players in terms of simulation play. The virtual weapons selected by those players are identified and a recommendation of the weapon presented to the inexperienced player of the computer simulation.

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San Mateo, CA, United States of America

Lakshmish Kaushik