Publication detail

Improving Noise Robustness of Automatic Speech Recognition via Parallel Data and Teacher-student Learning

MOŠNER, L. WU, M. RAJU, A. PARTHASARATHI, S. KUMATANI, K. SUNDARAM, S. MAAS, R. HOFFMEISTER, B.

Original Title

Improving Noise Robustness of Automatic Speech Recognition via Parallel Data and Teacher-student Learning

Type

conference paper

Language

English

Original Abstract

For real-world speech recognition applications, noise robustness is still a challenge. In this work, we adopt the teacherstudent (T/S) learning technique using a parallel clean and noisy corpus for improving automatic speech recognition (ASR) performance under multimedia noise. On top of that, we apply a logits selection method which only preserves the k highest values to prevent wrong emphasis of knowledge from the teacher and to reduce bandwidth needed for transferring data. We incorporate up to 8000 hours of untranscribed data for training and present our results on sequence trained models apart from cross entropy trained ones. The best sequence trained student model yields relative word error rate (WER) reductions of approximately 10.1%, 28.7% and 19.6% on our clean, simulated noisy and real test sets respectively comparing to a sequence trained teacher.

Keywords

automatic speech recognition, noise robustness, teacher-student training, domain adaptation

Authors

MOŠNER, L.; WU, M.; RAJU, A.; PARTHASARATHI, S.; KUMATANI, K.; SUNDARAM, S.; MAAS, R.; HOFFMEISTER, B.

Released

12. 5. 2019

Publisher

IEEE Signal Processing Society

Location

Brighton

ISBN

978-1-5386-4658-8

Book

Proceedings of ICASSP

Pages from

6475

Pages to

6479

Pages count

5

URL

https://ieeexplore.ieee.org/document/8683422

BibTex

@inproceedings{BUT160006,
  author="MOŠNER, L. and WU, M. and RAJU, A. and PARTHASARATHI, S. and KUMATANI, K. and SUNDARAM, S. and MAAS, R. and HOFFMEISTER, B.",
  title="Improving Noise Robustness of Automatic Speech Recognition via Parallel Data and Teacher-student Learning",
  booktitle="Proceedings of ICASSP",
  year="2019",
  pages="6475--6479",
  publisher="IEEE Signal Processing Society",
  address="Brighton",
  doi="10.1109/ICASSP.2019.8683422",
  isbn="978-1-5386-4658-8",
  url="https://ieeexplore.ieee.org/document/8683422"
}