Detail publikace

Investigation of Specaugment for Deep Speaker Embedding Learning

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

Originální název

Investigation of Specaugment for Deep Speaker Embedding Learning

Typ

článek ve sborníku ve WoS nebo Scopus

Jazyk

angličtina

Originální abstrakt

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.

Klíčová slova

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

Autoři

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

Vydáno

4. 5. 2020

Nakladatel

IEEE Signal Processing Society

Místo

Barcelona

ISBN

978-1-5090-6631-5

Kniha

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

Strany od

7139

Strany do

7143

Strany počet

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"
}