Microsoft Technology Licensing, LLC

Anthony Phillip Stark

Rui Zhao

Yifan Gong

Hosam Adel Khalil

Khuram Shahid

Emilian Y Stoimenov

Guoli Ye

Kaustubh Prakash Kalgaonkar

Mahadevan Srinivasan

Ivaylo Andreanov Enchev

Generally discussed herein are devices, systems, and methods for on-device detection of a wake word. A device can include a memory including model parameters that define a custom wake word detection model, the wake word detection model including a recurrent neural network transducer (RNNT) and a lookup table (LUT), the LUT indicating a hidden vector to be provided in response to a phoneme of a user-specified wake word, a microphone to capture audio, and processing circuitry to receive the audio from the microphone, determine, using the wake word detection model, whether the audio includes an utterance of the user-specified wake word, and wake up a personal assistant after determining the audio includes the utterance of the user-specified wake word.

On-device custom wake word detection

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Redmond, WA, United States of America

Anthony Phillip Stark