Publication detail

Speaker embeddings by modeling channel-wise correlations

STAFYLAKIS, T. ROHDIN, J. BURGET, L.

Original Title

Speaker embeddings by modeling channel-wise correlations

Type

conference paper

Language

English

Original Abstract

Speaker embeddings extracted with deep 2D convolutional neural networks are typically modeled as projections of first and second order statistics of channel-frequency pairs onto a linear layer, using either average or attentive pooling along the time axis. In this paper we examine an alternative pooling method, where pairwise correlations between channels for given frequencies are used as statistics. The method is inspired by style-transfer methods in computer vision, where the style of an image, modeled by the matrix of channel-wise correlations, is transferred to another image, in order to produce a new image having the style of the first and the content of the second. By drawing analogies between image style and speaker characteristics, and between image content and phonetic sequence, we explore the use of such channel-wise correlations features to train a ResNet architecture in an end-to-end fashion. Our experiments on VoxCeleb demonstrate the effectiveness of the proposed pooling method in speaker recognition.

Keywords

speaker recognition, style-transfer, deep learning

Authors

STAFYLAKIS, T.; ROHDIN, J.; BURGET, L.

Released

30. 8. 2021

Publisher

International Speech Communication Association

Location

Brno

ISBN

1990-9772

Periodical

Proceedings of Interspeech

Year of study

2021

Number

8

State

French Republic

Pages from

501

Pages to

505

Pages count

5

URL

BibTex

@inproceedings{BUT175834,
  author="Themos {Stafylakis} and Johan Andréas {Rohdin} and Lukáš {Burget}",
  title="Speaker embeddings by modeling channel-wise correlations",
  booktitle="Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH",
  year="2021",
  journal="Proceedings of Interspeech",
  volume="2021",
  number="8",
  pages="501--505",
  publisher="International Speech Communication Association",
  address="Brno",
  doi="10.21437/Interspeech.2021-1442",
  issn="1990-9772",
  url="https://www.isca-speech.org/archive/interspeech_2021/stafylakis21_interspeech.html"
}

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