@inproceedings{stein-usabaev-2012-automatic,
title = "Automatic Speech Recognition on a Firefighter {TETRA} Broadcast Channel",
author = "Stein, Daniel and
Usabaev, Bela",
editor = "Calzolari, Nicoletta and
Choukri, Khalid and
Declerck, Thierry and
Do{\u{g}}an, Mehmet U{\u{g}}ur and
Maegaard, Bente and
Mariani, Joseph and
Moreno, Asuncion and
Odijk, Jan and
Piperidis, Stelios",
booktitle = "Proceedings of the Eighth International Conference on Language Resources and Evaluation ({LREC}'12)",
month = may,
year = "2012",
address = "Istanbul, Turkey",
publisher = "European Language Resources Association (ELRA)",
url = "http://www.lrec-conf.org/proceedings/lrec2012/pdf/113_Paper.pdf",
pages = "119--124",
abstract = "For a reliable keyword extraction on firefighter radio communication, a strong automatic speech recognition system is needed. However, real-life data poses several challenges like a distorted voice signal, background noise and several different speakers. Moreover, the domain is out-of-scope for common language models, and the available data is scarce. In this paper, we introduce the PRONTO corpus, which consists of German firefighter exercise transcriptions. We show that by standard adaption techniques the recognition rate already rises from virtually zero to up to 51.7{\%} and can be further improved by domain-specific rules to 47.9{\%}. Extending the acoustic material by semi-automatic transcription and crawled in-domain written material, we arrive at a WER of 45.2{\%}.",
}
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<abstract>For a reliable keyword extraction on firefighter radio communication, a strong automatic speech recognition system is needed. However, real-life data poses several challenges like a distorted voice signal, background noise and several different speakers. Moreover, the domain is out-of-scope for common language models, and the available data is scarce. In this paper, we introduce the PRONTO corpus, which consists of German firefighter exercise transcriptions. We show that by standard adaption techniques the recognition rate already rises from virtually zero to up to 51.7% and can be further improved by domain-specific rules to 47.9%. Extending the acoustic material by semi-automatic transcription and crawled in-domain written material, we arrive at a WER of 45.2%.</abstract>
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%0 Conference Proceedings
%T Automatic Speech Recognition on a Firefighter TETRA Broadcast Channel
%A Stein, Daniel
%A Usabaev, Bela
%Y Calzolari, Nicoletta
%Y Choukri, Khalid
%Y Declerck, Thierry
%Y Doğan, Mehmet Uğur
%Y Maegaard, Bente
%Y Mariani, Joseph
%Y Moreno, Asuncion
%Y Odijk, Jan
%Y Piperidis, Stelios
%S Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC’12)
%D 2012
%8 May
%I European Language Resources Association (ELRA)
%C Istanbul, Turkey
%F stein-usabaev-2012-automatic
%X For a reliable keyword extraction on firefighter radio communication, a strong automatic speech recognition system is needed. However, real-life data poses several challenges like a distorted voice signal, background noise and several different speakers. Moreover, the domain is out-of-scope for common language models, and the available data is scarce. In this paper, we introduce the PRONTO corpus, which consists of German firefighter exercise transcriptions. We show that by standard adaption techniques the recognition rate already rises from virtually zero to up to 51.7% and can be further improved by domain-specific rules to 47.9%. Extending the acoustic material by semi-automatic transcription and crawled in-domain written material, we arrive at a WER of 45.2%.
%U http://www.lrec-conf.org/proceedings/lrec2012/pdf/113_Paper.pdf
%P 119-124
Markdown (Informal)
[Automatic Speech Recognition on a Firefighter TETRA Broadcast Channel](http://www.lrec-conf.org/proceedings/lrec2012/pdf/113_Paper.pdf) (Stein & Usabaev, LREC 2012)
ACL