Publication detail

How To Improve Your Speaker Embeddings Extractor in Generic Toolkits

ZEINALI, H. BURGET, L. ROHDIN, J. STAFYLAKIS, T. ČERNOCKÝ, J.

Original Title

How To Improve Your Speaker Embeddings Extractor in Generic Toolkits

Type

conference paper

Language

English

Original Abstract

Recently, speaker embeddings extracted with deep neural networks became the state-of-the-art method for speaker verification. In this paper we aim to facilitate its implementation on a more generic toolkit than Kaldi, which we anticipate to enable further improvements on the method. We examine several tricks in training, such as the effects of normalizing input features and pooled statistics, different methods for preventing overfitting as well as alternative nonlinearities that can be used instead of Rectifier Linear Units. In addition, we investigate the difference in performance between TDNN and CNN, and between two types of attention mechanism. Experimental results on Speaker in the Wild, SRE 2016 and SRE 2018 datasets demonstrate the effectiveness of the proposed implementation.

Keywords

Deep neural network, speaker embedding, xvector, Tensorflow, Kaldi.

Authors

ZEINALI, H.; BURGET, L.; ROHDIN, J.; STAFYLAKIS, T.; ČERNOCKÝ, J.

Released

12. 5. 2019

Publisher

IEEE Signal Processing Society

Location

Brighton

ISBN

978-1-5386-4658-8

Book

Proceedings of 2019 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP)

Pages from

6141

Pages to

6145

Pages count

5

URL

https://ieeexplore.ieee.org/abstract/document/8683445

BibTex

@inproceedings{BUT158087,
  author="Hossein {Zeinali} and Lukáš {Burget} and Johan Andréas {Rohdin} and Themos {Stafylakis} and Jan {Černocký}",
  title="How To Improve Your Speaker Embeddings Extractor in Generic Toolkits",
  booktitle="Proceedings of 2019 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP)",
  year="2019",
  pages="6141--6145",
  publisher="IEEE Signal Processing Society",
  address="Brighton",
  doi="10.1109/ICASSP.2019.8683445",
  isbn="978-1-5386-4658-8",
  url="https://ieeexplore.ieee.org/abstract/document/8683445"
}