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Few-shot short utterance speaker verification using meta-learning
Short utterance speaker verification (SV) in the actual application is the task of accepting or rejecting the identity claim of a speaker based on a few enrollment utterances. Traditional methods have used deep neural networks to extract speaker representations for verification. Recently, several me...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
PeerJ Inc.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10280689/ https://www.ncbi.nlm.nih.gov/pubmed/37346533 http://dx.doi.org/10.7717/peerj-cs.1276 |
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author | Wang, Weijie Zhao, Hong Yang, Yikun Chang, YouKang You, Haojie |
author_facet | Wang, Weijie Zhao, Hong Yang, Yikun Chang, YouKang You, Haojie |
author_sort | Wang, Weijie |
collection | PubMed |
description | Short utterance speaker verification (SV) in the actual application is the task of accepting or rejecting the identity claim of a speaker based on a few enrollment utterances. Traditional methods have used deep neural networks to extract speaker representations for verification. Recently, several meta-learning approaches have learned a deep distance metric to distinguish speakers within meta-tasks. Among them, a prototypical network learns a metric space that may be used to compute the distance to the prototype center of speakers, in order to classify speaker identity. We use emphasized channel attention, propagation and aggregation in TDNN (ECAPA-TDNN) to implement the necessary function for the prototypical network, which is a nonlinear mapping from the input space to the metric space for either few-shot SV task. In addition, optimizing only for speakers in given meta-tasks cannot be sufficient to learn distinctive speaker features. Thus, we used an episodic training strategy, in which the classes of the support and query sets correspond to the classes of the entire training set, further improving the model performance. The proposed model outperforms comparison models on the VoxCeleb1 dataset and has a wide range of practical applications. |
format | Online Article Text |
id | pubmed-10280689 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | PeerJ Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-102806892023-06-21 Few-shot short utterance speaker verification using meta-learning Wang, Weijie Zhao, Hong Yang, Yikun Chang, YouKang You, Haojie PeerJ Comput Sci Artificial Intelligence Short utterance speaker verification (SV) in the actual application is the task of accepting or rejecting the identity claim of a speaker based on a few enrollment utterances. Traditional methods have used deep neural networks to extract speaker representations for verification. Recently, several meta-learning approaches have learned a deep distance metric to distinguish speakers within meta-tasks. Among them, a prototypical network learns a metric space that may be used to compute the distance to the prototype center of speakers, in order to classify speaker identity. We use emphasized channel attention, propagation and aggregation in TDNN (ECAPA-TDNN) to implement the necessary function for the prototypical network, which is a nonlinear mapping from the input space to the metric space for either few-shot SV task. In addition, optimizing only for speakers in given meta-tasks cannot be sufficient to learn distinctive speaker features. Thus, we used an episodic training strategy, in which the classes of the support and query sets correspond to the classes of the entire training set, further improving the model performance. The proposed model outperforms comparison models on the VoxCeleb1 dataset and has a wide range of practical applications. PeerJ Inc. 2023-04-21 /pmc/articles/PMC10280689/ /pubmed/37346533 http://dx.doi.org/10.7717/peerj-cs.1276 Text en ©2023 Wang et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Computer Science) and either DOI or URL of the article must be cited. |
spellingShingle | Artificial Intelligence Wang, Weijie Zhao, Hong Yang, Yikun Chang, YouKang You, Haojie Few-shot short utterance speaker verification using meta-learning |
title | Few-shot short utterance speaker verification using meta-learning |
title_full | Few-shot short utterance speaker verification using meta-learning |
title_fullStr | Few-shot short utterance speaker verification using meta-learning |
title_full_unstemmed | Few-shot short utterance speaker verification using meta-learning |
title_short | Few-shot short utterance speaker verification using meta-learning |
title_sort | few-shot short utterance speaker verification using meta-learning |
topic | Artificial Intelligence |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10280689/ https://www.ncbi.nlm.nih.gov/pubmed/37346533 http://dx.doi.org/10.7717/peerj-cs.1276 |
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