Amazon Technologies, Inc.

Jason Krone

Shiv Naga Prasad Vitaladevuni

Ming Sun

Yixin Gao

Yuzong Liu

Yi Zhang

Peng Zhang

Roger Scott Jenke

Meghana Puvvadi

Kartik Natarajan

Shang-Wen Daniel Li

Niranjhana Nayar

Garima Lalwani

Yi-An Lai

Techniques for updating a machine learning model based on user interactions are described. In particular, in some examples, user interactions with a chatbot provide aspects of a data set to be used to train or fine-tune a ML model. In some examples, this is accomplished by collecting data from a first plurality of interactions with a machine learning (ML) model; generating a variant of the ML model using the collected data by: filtering the collected data to create a first data set, training the ML model based on the first data set to generate an adapted ML model, and fine-tuning the adapted ML model on a second data set, different than the first data set to generate the variant of the ML model.

Adaptations of task-oriented agents using user interactions

A system and method performs multilingual wakeword detection by determining a language corresponding to the wakeword. A first wakeword-detection component, which may execute using a digital-signal processor, determines that audio data includes a representation of the wakeword and determines a language corresponding to the wakeword. A second, more accurate wakeword-detection component may then process the audio data using the language to confirm that it includes the representation of the wakeword. The audio data may then be sent to a remote system for further processing.

Multilingual wakeword detection

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Pacific Grove, CA, United States of America

Jason Krone