KALAKA-2: a TV Broadcast Speech Database for the Recognition of Iberian Languages in Clean and Noisy Environments

13743

Authors

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Abstract

AbstractThis paper presents the main features (design issues, recording setup, etc.) of KALAKA-2, a TV broadcast speech database specifically designed for the development and evaluation of language recognition systems in clean and noisy environments. KALAKA-2 was created to support the Albayzin 2010 Language Recognition Evaluation (LRE), organized by the Spanish Network on Speech Technologies from June to November 2010. The database features 6 target languages: Basque, Catalan, English, Galician, Portuguese and Spanish, and includes segments in other (Out-Of-Set) languages, which allow to perform open-set verification tests. The best performance attained in the Albayzin 2010 LRE is presented and briefly discussed. The performance of a state-of-the-art system in various tasks defined on the database is also presented. In both cases, results highlight the suitability of KALAKA-2 as a benchmark for the development and evaluation of language recognition technology.

Keywords

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Full Content

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Labeled Text

AbstractThis paper presents the main features (design issues, recording setup, etc.) of KALAKA-2, a TV broadcast speech database specifically designed for the development and evaluation of language recognition systems in clean and noisy environments. KALAKA-2 was created to support the Albayzin 2010 Language Recognition Evaluation (LRE), organized by the Spanish Network on Speech Technologies from June to November 2010. The database features 6 target languages: Basque, Catalan, English, Galician, Portuguese and Spanish, and includes segments in other (Out-Of-Set) languages, which allow to perform open-set verification tests. The best performance attained in the Albayzin 2010 LRE is presented and briefly discussed. The performance of a state-of-the-art system in various tasks defined on the database is also presented. In both cases, results highlight the suitability of KALAKA-2 as a benchmark for the development and evaluation of language recognition technology.

Publication Details

Paper ID:
13743
Year:
2012
Publication:
TIPSTER
Publication Type:
conf
DOI:
nan