Publication detail

Investigation of Specaugment for Deep Speaker Embedding Learning

WANG, S. ROHDIN, J. PLCHOT, O. BURGET, L. YU, K. ČERNOCKÝ, J.

Original Title

Investigation of Specaugment for Deep Speaker Embedding Learning

Type

conference paper

Language

English

Original Abstract

SpecAugment is a newly proposed data augmentation method for speech recognition. By randomly masking bands in the log Mel spectogram this method leads to impressive performance improvements. In this paper, we investigate the usage of SpecAugment for speaker verification tasks. Two different models, namely 1-D convolutional TDNN and 2-D convolutional ResNet34, trained with either Softmax or AAM-Softmax loss, are used to analyze SpecAugments effectiveness. Experiments are carried out on the Voxceleb and NIST SRE 2016 dataset. By applying SpecAugment to the original clean data in an on-the-fly manner without complex off-line data augmentation methods, we obtained 3.72% and 11.49% EER for NIST SRE 2016 Cantonese and Tagalog, respectively. For Voxceleb1 evaluation set, we obtained 1.47% EER.

Keywords

speaker embedding, on-the-fly data augmentation, speaker verification, specaugment

Authors

WANG, S.; ROHDIN, J.; PLCHOT, O.; BURGET, L.; YU, K.; ČERNOCKÝ, J.

Released

4. 5. 2020

Publisher

IEEE Signal Processing Society

Location

Barcelona

ISBN

978-1-5090-6631-5

Book

ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings

Pages from

7139

Pages to

7143

Pages count

5

URL

https://ieeexplore.ieee.org/document/9053481/authors#authors

BibTex

@inproceedings{BUT163947,
  author="WANG, S. and ROHDIN, J. and PLCHOT, O. and BURGET, L. and YU, K. and ČERNOCKÝ, J.",
  title="Investigation of Specaugment for Deep Speaker Embedding Learning",
  booktitle="ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",
  year="2020",
  pages="7139--7143",
  publisher="IEEE Signal Processing Society",
  address="Barcelona",
  doi="10.1109/ICASSP40776.2020.9053481",
  isbn="978-1-5090-6631-5",
  url="https://ieeexplore.ieee.org/document/9053481/authors#authors"
}

Documents

wang_icassp2020_09053481.pdf