Intel Corporation

Gabriel Amores

Guillermo Perez

Moshe Wasserblat

Michael E Deisher

Loic Dufresne De Virel

An apparatus for speech model with personalization via ambient context harvesting, is described herein. The apparatus includes a microphone, context harvesting module, confidence module, and training module. The context harvesting module is to determine a context associated with the captured audio signals. A confidence module is to determine a confidence of the context as applied to the audio signals. A training module is to train a neural network in response to the confidence being above a predetermined threshold.

Speech model personalization via ambient context harvesting

Methods, systems and articles of manufacture for a communication system that allows a robot to interact with users via a single party dialogue strategy or a multi-party dialogue strategy are disclosed. An example communication system includes a distance tracker to determine whether any of a plurality of parties has come within a conversation distance of the communication system. The example communication system can also include a first dialogue handler in communication with a first memory storing a single party dialogue processing algorithm and a second dialogue handler in communication with a second memory storing a multi-party dialogue processing algorithm. Based on information supplied by the distance tracker, a dialogue mode selector selects between the first dialogue handler and the second dialogue handler to process conversations between the robot and a first party and the robot and a second party.

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Seville, Spain

Gabriel Amores