Samsung Electronics Co., Ltd.

Gowtham Srinivasan

Anil Sunder Yadav

Srinivasa Rao Ponakala

Sivakumar Balasubramanian

Vijendra R Apsingekar

Aditya Jajodia

A method includes accessing, using at least one processor of an electronic device, a machine learning model. The machine learning model is a trained student model that is trained using audio samples in a plurality of accent types. The method also includes receiving, using the at least one processor, an audio input from an audio input device. The method further includes providing, using the at least one processor, the audio input to the trained student model. The method also includes receiving, using the at least one processor, an output from the trained student model including frame-level probabilities associated with the audio input. In addition, the method includes instructing, using the at least one processor, at least one action based on the frame-level probabilities associated with the audio input.

System and method for accent-agnostic frame-level wake word detection

A method of generating a trained trigger word detection model includes training an auxiliary model, based on an auxiliary task, to concentrate on one or more utterances and/or learn context of the one or more utterances using generic single word and/or phrase training data; and obtaining a trigger word detection model by retraining one or more final layers of the auxiliary model, which is weighted based on the auxiliary task, based on a trigger word detection task that detects one or more trigger words. The retraining uses training data specific to the one or more trigger words.

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Gowtham Srinivasan