@inproceedings{jones-etal-2020-call,
title = "Call My Net 2: A New Resource for Speaker Recognition",
author = "Jones, Karen and
Strassel, Stephanie and
Walker, Kevin and
Wright, Jonathan",
editor = "Calzolari, Nicoletta and
B{\'e}chet, Fr{\'e}d{\'e}ric and
Blache, Philippe and
Choukri, Khalid and
Cieri, Christopher and
Declerck, Thierry and
Goggi, Sara and
Isahara, Hitoshi and
Maegaard, Bente and
Mariani, Joseph and
Mazo, H{\'e}l{\`e}ne and
Moreno, Asuncion and
Odijk, Jan and
Piperidis, Stelios",
booktitle = "Proceedings of the Twelfth Language Resources and Evaluation Conference",
month = may,
year = "2020",
address = "Marseille, France",
publisher = "European Language Resources Association",
url = "https://aclanthology.org/2020.lrec-1.816/",
pages = "6621--6626",
language = "eng",
ISBN = "979-10-95546-34-4",
abstract = "We introduce the Call My Net 2 (CMN2) Corpus, a new resource for speaker recognition featuring Tunisian Arabic conversations between friends and family, incorporating both traditional telephony and VoIP data. The corpus contains data from over 400 Tunisian Arabic speakers collected via a custom-built platform deployed in Tunis, with each speaker making 10 or more calls each lasting up to 10 minutes. Calls include speech in various realistic and natural acoustic settings, both noisy and non-noisy. Speakers used a variety of handsets, including landline and mobile devices, and made VoIP calls from tablets or computers. All calls were subject to a series of manual and automatic quality checks, including speech duration, audio quality, language identity and speaker identity. The CMN2 corpus has been used in two NIST Speaker Recognition Evaluations (SRE18 and SRE19), and the SRE test sets as well as the full CMN2 corpus will be published in the Linguistic Data Consortium Catalog. We describe CMN2 corpus requirements, the telephone collection platform, and procedures for call collection. We review properties of the CMN2 dataset and discuss features of the corpus that distinguish it from prior SRE collection efforts, including some of the technical challenges encountered with collecting VoIP data."
}
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<abstract>We introduce the Call My Net 2 (CMN2) Corpus, a new resource for speaker recognition featuring Tunisian Arabic conversations between friends and family, incorporating both traditional telephony and VoIP data. The corpus contains data from over 400 Tunisian Arabic speakers collected via a custom-built platform deployed in Tunis, with each speaker making 10 or more calls each lasting up to 10 minutes. Calls include speech in various realistic and natural acoustic settings, both noisy and non-noisy. Speakers used a variety of handsets, including landline and mobile devices, and made VoIP calls from tablets or computers. All calls were subject to a series of manual and automatic quality checks, including speech duration, audio quality, language identity and speaker identity. The CMN2 corpus has been used in two NIST Speaker Recognition Evaluations (SRE18 and SRE19), and the SRE test sets as well as the full CMN2 corpus will be published in the Linguistic Data Consortium Catalog. We describe CMN2 corpus requirements, the telephone collection platform, and procedures for call collection. We review properties of the CMN2 dataset and discuss features of the corpus that distinguish it from prior SRE collection efforts, including some of the technical challenges encountered with collecting VoIP data.</abstract>
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%0 Conference Proceedings
%T Call My Net 2: A New Resource for Speaker Recognition
%A Jones, Karen
%A Strassel, Stephanie
%A Walker, Kevin
%A Wright, Jonathan
%Y Calzolari, Nicoletta
%Y Béchet, Frédéric
%Y Blache, Philippe
%Y Choukri, Khalid
%Y Cieri, Christopher
%Y Declerck, Thierry
%Y Goggi, Sara
%Y Isahara, Hitoshi
%Y Maegaard, Bente
%Y Mariani, Joseph
%Y Mazo, Hélène
%Y Moreno, Asuncion
%Y Odijk, Jan
%Y Piperidis, Stelios
%S Proceedings of the Twelfth Language Resources and Evaluation Conference
%D 2020
%8 May
%I European Language Resources Association
%C Marseille, France
%@ 979-10-95546-34-4
%G eng
%F jones-etal-2020-call
%X We introduce the Call My Net 2 (CMN2) Corpus, a new resource for speaker recognition featuring Tunisian Arabic conversations between friends and family, incorporating both traditional telephony and VoIP data. The corpus contains data from over 400 Tunisian Arabic speakers collected via a custom-built platform deployed in Tunis, with each speaker making 10 or more calls each lasting up to 10 minutes. Calls include speech in various realistic and natural acoustic settings, both noisy and non-noisy. Speakers used a variety of handsets, including landline and mobile devices, and made VoIP calls from tablets or computers. All calls were subject to a series of manual and automatic quality checks, including speech duration, audio quality, language identity and speaker identity. The CMN2 corpus has been used in two NIST Speaker Recognition Evaluations (SRE18 and SRE19), and the SRE test sets as well as the full CMN2 corpus will be published in the Linguistic Data Consortium Catalog. We describe CMN2 corpus requirements, the telephone collection platform, and procedures for call collection. We review properties of the CMN2 dataset and discuss features of the corpus that distinguish it from prior SRE collection efforts, including some of the technical challenges encountered with collecting VoIP data.
%U https://aclanthology.org/2020.lrec-1.816/
%P 6621-6626
Markdown (Informal)
[Call My Net 2: A New Resource for Speaker Recognition](https://aclanthology.org/2020.lrec-1.816/) (Jones et al., LREC 2020)
ACL
- Karen Jones, Stephanie Strassel, Kevin Walker, and Jonathan Wright. 2020. Call My Net 2: A New Resource for Speaker Recognition. In Proceedings of the Twelfth Language Resources and Evaluation Conference, pages 6621–6626, Marseille, France. European Language Resources Association.