Improving Speaker Verification with Self-Pretrained Transformer Models
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F23%3APU149718" target="_blank" >RIV/00216305:26230/23:PU149718 - isvavai.cz</a>
Result on the web
<a href="https://www.isca-speech.org/archive/pdfs/interspeech_2023/peng23_interspeech.pdf" target="_blank" >https://www.isca-speech.org/archive/pdfs/interspeech_2023/peng23_interspeech.pdf</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.21437/Interspeech.2023-453" target="_blank" >10.21437/Interspeech.2023-453</a>
Alternative languages
Result language
angličtina
Original language name
Improving Speaker Verification with Self-Pretrained Transformer Models
Original language description
Recently, fine-tuning large pre-trained Transformer models using downstream datasets has received a rising interest. Despite their success, it is still challenging to disentangle the benefits of large-scale datasets and Transformer structures from the limitations of the pre-training. In this paper, we introduce a hierarchical training approach, named self-pretraining, in which Transformer models are pretrained and finetuned on the same dataset. Three pre-trained models including HuBERT, Conformer andWavLM are evaluated on four different speaker verification datasets with varying sizes. Our experiments show that these self-pretrained models achieve competitive performance on downstream speaker verification tasks with only one-third of the data compared to Librispeech pretraining, such as Vox- Celeb1 and CNCeleb1. Furthermore, when pre-training only on the VoxCeleb2-dev, the Conformer model outperforms the one pre-trained on 94k hours of data using the same fine-tuning settings.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
Result was created during the realization of more than one project. More information in the Projects tab.
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH
ISBN
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ISSN
1990-9772
e-ISSN
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Number of pages
5
Pages from-to
5361-5365
Publisher name
International Speech Communication Association
Place of publication
Dublin
Event location
Dublin
Event date
Aug 20, 2023
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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