@inproceedings{abejon-etal-2010-study,
title = "A Study of the Influence of Speech Type on Automatic Language Recognition Performance",
author = "Abej{\'o}n, Alejandro and
Toledano, Doroteo T. and
Spada, Danilo and
Victor, Gonz{\'a}lez and
L{\'o}pez, Daniel Hern{\'a}ndez",
editor = "Calzolari, Nicoletta and
Choukri, Khalid and
Maegaard, Bente and
Mariani, Joseph and
Odijk, Jan and
Piperidis, Stelios and
Rosner, Mike and
Tapias, Daniel",
booktitle = "Proceedings of the Seventh International Conference on Language Resources and Evaluation ({LREC}'10)",
month = may,
year = "2010",
address = "Valletta, Malta",
publisher = "European Language Resources Association (ELRA)",
url = "http://www.lrec-conf.org/proceedings/lrec2010/pdf/174_Paper.pdf",
abstract = "Automatic language recognition on spontaneous speech has experienced a rapid development in the last few years. This development has been in part due to the competitive technological Language Recognition Evaluations (LRE) organized by the National Institute of Standards and Technology (NIST). Until now, the need to have clearly defined and consistent evaluations has kept some real-life application issues out of these evaluations. In particular, all past NIST LREs have used exclusively conversational telephone speech (CTS) for development and test. Fortunately this has changed in the current NIST LRE since it includes also broadcast speech. However, for testing only the telephone speech found in broadcast data will be used. In real-life applications, there could be several more types of speech and systems could be forced to use a mix of different types of data for training and development and recognition. In this article, we have defined a test-bed including several types of speech data and have analyzed how a typical language recognition system works using different types of speech, and also a combination of different types of speech, for training and testing.",
}
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<abstract>Automatic language recognition on spontaneous speech has experienced a rapid development in the last few years. This development has been in part due to the competitive technological Language Recognition Evaluations (LRE) organized by the National Institute of Standards and Technology (NIST). Until now, the need to have clearly defined and consistent evaluations has kept some real-life application issues out of these evaluations. In particular, all past NIST LREs have used exclusively conversational telephone speech (CTS) for development and test. Fortunately this has changed in the current NIST LRE since it includes also broadcast speech. However, for testing only the telephone speech found in broadcast data will be used. In real-life applications, there could be several more types of speech and systems could be forced to use a mix of different types of data for training and development and recognition. In this article, we have defined a test-bed including several types of speech data and have analyzed how a typical language recognition system works using different types of speech, and also a combination of different types of speech, for training and testing.</abstract>
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%0 Conference Proceedings
%T A Study of the Influence of Speech Type on Automatic Language Recognition Performance
%A Abejón, Alejandro
%A Toledano, Doroteo T.
%A Spada, Danilo
%A Victor, González
%A López, Daniel Hernández
%Y Calzolari, Nicoletta
%Y Choukri, Khalid
%Y Maegaard, Bente
%Y Mariani, Joseph
%Y Odijk, Jan
%Y Piperidis, Stelios
%Y Rosner, Mike
%Y Tapias, Daniel
%S Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC’10)
%D 2010
%8 May
%I European Language Resources Association (ELRA)
%C Valletta, Malta
%F abejon-etal-2010-study
%X Automatic language recognition on spontaneous speech has experienced a rapid development in the last few years. This development has been in part due to the competitive technological Language Recognition Evaluations (LRE) organized by the National Institute of Standards and Technology (NIST). Until now, the need to have clearly defined and consistent evaluations has kept some real-life application issues out of these evaluations. In particular, all past NIST LREs have used exclusively conversational telephone speech (CTS) for development and test. Fortunately this has changed in the current NIST LRE since it includes also broadcast speech. However, for testing only the telephone speech found in broadcast data will be used. In real-life applications, there could be several more types of speech and systems could be forced to use a mix of different types of data for training and development and recognition. In this article, we have defined a test-bed including several types of speech data and have analyzed how a typical language recognition system works using different types of speech, and also a combination of different types of speech, for training and testing.
%U http://www.lrec-conf.org/proceedings/lrec2010/pdf/174_Paper.pdf
Markdown (Informal)
[A Study of the Influence of Speech Type on Automatic Language Recognition Performance](http://www.lrec-conf.org/proceedings/lrec2010/pdf/174_Paper.pdf) (Abejón et al., LREC 2010)
ACL