Trainable frontend for robust and far-field keyword spotting

Reference

Yuxuan Wang, Pascal Getreuer, Thad Hughes, Richard F. Lyon, Rif A. Saurous, “Trainable frontend for robust and far-field keyword spotting.” 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 5670–5674, 2017.

Article permalink: https://doi.org/10.1109/ICASSP.2017.7953242

@inproceedings{wang2017trainable,
  title={Trainable frontend for robust and far-field keyword spotting},
  author={Wang, Yuxuan and Getreuer, Pascal and Hughes, Thad and Lyon,
          Richard F and Saurous, Rif A},
  booktitle={2017 IEEE International Conference on
             Acoustics, Speech and Signal Processing (ICASSP)},
  pages={5670--5674},
  year={2017},
  organization={IEEE}
}

Abstract

Robust and far-field speech recognition is critical to enable true hands-free communication. In far-field conditions, signals are attenuated due to distance. To improve robustness to loudness variation, we introduce a novel frontend called per-channel energy normalization (PCEN). The key ingredient of PCEN is the use of an automatic gain control based dynamic compression to replace the widely used static (such as log or root) compression. We evaluate PCEN on the keyword spotting task. On our large rerecorded noisy and far-field eval sets, we show that PCEN significantly improves recognition performance. Furthermore, we model PCEN as neural network layers and optimize high-dimensional PCEN parameters jointly with the keyword spotting acoustic model. The trained PCEN frontend demonstrates significant further improvements without increasing model complexity or inference-time cost.


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