The speakers in the wild (SITW) speaker recognition database

The Speakers in the Wild (SITW) speaker recognition database contains hand-annotated speech samples from open-source media for the purpose of benchmarking text-independent speaker recognition technology on single and multi-speaker audio acquired across unconstrained or "wild" conditions. T...

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Detalles Bibliográficos
Autores principales: McLaren, M., Ferrer, L., Castan, D., Lawson, A., Morgan N., Georgiou P., Narayanan S., Metze F., Amazon Alexa; Apple; eBay; et al.; Google; Microsoft
Formato: CONF
Materias:
Database
Real-world data
Speaker recognition
Character recognition
Database systems
Speech communication
Speech processing
Compression artifacts
Controlled conditions
Open sources
Real-world
Text independents
Speech recognition
Acceso en línea:http://hdl.handle.net/20.500.12110/paper_2308457X_v08-12-September-2016_n_p818_McLaren
Aporte de:
Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA) de Universidad de Buenos Aires
id todo:paper_2308457X_v08-12-September-2016_n_p818_McLaren
record_format dspace
spelling todo:paper_2308457X_v08-12-September-2016_n_p818_McLaren2023-10-03T16:40:52Z The speakers in the wild (SITW) speaker recognition database McLaren, M. Ferrer, L. Castan, D. Lawson, A. Morgan N. Georgiou P. Morgan N. Narayanan S. Metze F. Amazon Alexa; Apple; eBay; et al.; Google; Microsoft Database Real-world data Speaker recognition Character recognition Database systems Speech communication Speech processing Compression artifacts Controlled conditions Open sources Real-world Speaker recognition Text independents Speech recognition The Speakers in the Wild (SITW) speaker recognition database contains hand-annotated speech samples from open-source media for the purpose of benchmarking text-independent speaker recognition technology on single and multi-speaker audio acquired across unconstrained or "wild" conditions. The database consists of recordings of 299 speakers, with an average of eight different sessions per person. Unlike existing databases for speaker recognition, this data was not collected under controlled conditions and thus contains real noise, reverberation, intraspeaker variability and compression artifacts. These factors are often convolved in the real world, as the SITW data shows, and they make SITW a challenging database for single-and multispeaker recognition Copyright ©2016 ISCA. CONF info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by/2.5/ar http://hdl.handle.net/20.500.12110/paper_2308457X_v08-12-September-2016_n_p818_McLaren
institution Universidad de Buenos Aires
institution_str I-28
repository_str R-134
collection Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA)
topic Database
Real-world data
Speaker recognition
Character recognition
Database systems
Speech communication
Speech processing
Compression artifacts
Controlled conditions
Open sources
Real-world
Speaker recognition
Text independents
Speech recognition
spellingShingle Database
Real-world data
Speaker recognition
Character recognition
Database systems
Speech communication
Speech processing
Compression artifacts
Controlled conditions
Open sources
Real-world
Speaker recognition
Text independents
Speech recognition
McLaren, M.
Ferrer, L.
Castan, D.
Lawson, A.
Morgan N.
Georgiou P.
Morgan N.
Narayanan S.
Metze F.
Amazon Alexa; Apple; eBay; et al.; Google; Microsoft
The speakers in the wild (SITW) speaker recognition database
topic_facet Database
Real-world data
Speaker recognition
Character recognition
Database systems
Speech communication
Speech processing
Compression artifacts
Controlled conditions
Open sources
Real-world
Speaker recognition
Text independents
Speech recognition
description The Speakers in the Wild (SITW) speaker recognition database contains hand-annotated speech samples from open-source media for the purpose of benchmarking text-independent speaker recognition technology on single and multi-speaker audio acquired across unconstrained or "wild" conditions. The database consists of recordings of 299 speakers, with an average of eight different sessions per person. Unlike existing databases for speaker recognition, this data was not collected under controlled conditions and thus contains real noise, reverberation, intraspeaker variability and compression artifacts. These factors are often convolved in the real world, as the SITW data shows, and they make SITW a challenging database for single-and multispeaker recognition Copyright ©2016 ISCA.
format CONF
author McLaren, M.
Ferrer, L.
Castan, D.
Lawson, A.
Morgan N.
Georgiou P.
Morgan N.
Narayanan S.
Metze F.
Amazon Alexa; Apple; eBay; et al.; Google; Microsoft
author_facet McLaren, M.
Ferrer, L.
Castan, D.
Lawson, A.
Morgan N.
Georgiou P.
Morgan N.
Narayanan S.
Metze F.
Amazon Alexa; Apple; eBay; et al.; Google; Microsoft
author_sort McLaren, M.
title The speakers in the wild (SITW) speaker recognition database
title_short The speakers in the wild (SITW) speaker recognition database
title_full The speakers in the wild (SITW) speaker recognition database
title_fullStr The speakers in the wild (SITW) speaker recognition database
title_full_unstemmed The speakers in the wild (SITW) speaker recognition database
title_sort speakers in the wild (sitw) speaker recognition database
url http://hdl.handle.net/20.500.12110/paper_2308457X_v08-12-September-2016_n_p818_McLaren
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