ISCA Archive Interspeech 2022
ISCA Archive Interspeech 2022

Low-resource Low-footprint Wake-word Detection using Knowledge Distillation

Arindam Ghosh, Mark Fuhs, Deblin Bagchi, Bahman Farahani, Monika Woszczyna

As virtual assistants have become more diverse and specialized, so has the demand for application or brand-specific wake words. However, the wake-word-specific datasets typically used to train wake-word detectors are costly to create. In this paper, we explore two techniques to leverage acoustic modeling data for large-vocabulary speech recognition to improve a purpose-built wake-word detector: transfer learning and knowledge distillation. We also explore how these techniques interact with time-synchronous training targets to improve detection latency. Experiments are presented on the open-source "Hey Snips” dataset and a more challenging in-house far-field dataset. Using phone-synchronous targets and knowledge distillation from a large acoustic model, we are able to improve accuracy across dataset sizes for both datasets while reducing latency.

doi: 10.21437/Interspeech.2022-529

Cite as: Ghosh, A., Fuhs, M., Bagchi, D., Farahani, B., Woszczyna, M. (2022) Low-resource Low-footprint Wake-word Detection using Knowledge Distillation. Proc. Interspeech 2022, 3739-3743, doi: 10.21437/Interspeech.2022-529

  author={Arindam Ghosh and Mark Fuhs and Deblin Bagchi and Bahman Farahani and Monika Woszczyna},
  title={{Low-resource Low-footprint Wake-word Detection using Knowledge Distillation}},
  booktitle={Proc. Interspeech 2022},