ISCA Archive Interspeech 2021
ISCA Archive Interspeech 2021

Personalized Keyphrase Detection Using Speaker and Environment Information

Rajeev Rikhye, Quan Wang, Qiao Liang, Yanzhang He, Ding Zhao, Yiteng Huang, Arun Narayanan, Ian McGraw

In this paper, we introduce a streaming keyphrase detection system that can be easily customized to accurately detect any phrase composed of words from a large vocabulary. The system is implemented with an end-to-end trained automatic speech recognition (ASR) model and a text-independent speaker verification model. To address the challenge of detecting these keyphrases under various noisy conditions, a speaker separation model is added to the feature frontend of the speaker verification model, and an adaptive noise cancellation (ANC) algorithm is included to exploit cross-microphone noise coherence. Our experiments show that the text-independent speaker verification model largely reduces the false triggering rate of the keyphrase detection, while the speaker separation model and adaptive noise cancellation largely reduce false rejections.


doi: 10.21437/Interspeech.2021-204

Cite as: Rikhye, R., Wang, Q., Liang, Q., He, Y., Zhao, D., Huang, Y., Narayanan, A., McGraw, I. (2021) Personalized Keyphrase Detection Using Speaker and Environment Information. Proc. Interspeech 2021, 4204-4208, doi: 10.21437/Interspeech.2021-204

@inproceedings{rikhye21_interspeech,
  author={Rajeev Rikhye and Quan Wang and Qiao Liang and Yanzhang He and Ding Zhao and Yiteng Huang and Arun Narayanan and Ian McGraw},
  title={{Personalized Keyphrase Detection Using Speaker and Environment Information}},
  year=2021,
  booktitle={Proc. Interspeech 2021},
  pages={4204--4208},
  doi={10.21437/Interspeech.2021-204}
}