@inproceedings{nguyen-etal-2016-ioit,
title = "The {IOIT} {E}nglish {ASR} system for {IWSLT} 2016",
author = "Nguyen, Van Huy and
Phung, Trung-Nghia and
Vu, Tat Thang and
Luong, Chi Mai",
editor = {Cettolo, Mauro and
Niehues, Jan and
St{\"u}ker, Sebastian and
Bentivogli, Luisa and
Cattoni, Rolando and
Federico, Marcello},
booktitle = "Proceedings of the 13th International Conference on Spoken Language Translation",
month = dec # " 8-9",
year = "2016",
address = "Seattle, Washington D.C",
publisher = "International Workshop on Spoken Language Translation",
url = "https://aclanthology.org/2016.iwslt-1.14",
abstract = "This paper describes the speech recognition system of IOIT for IWSLT 2016. Four single DNN-based systems were developed to produce the 1st-pass lattices for the test sets using a baseline language model. The 2nd-pass lattices were further obtained by applying N-best list rescoring on topic adapted language models which were constructed from closed topic sentences by applying a text selection method. The final transcriptions of test sets were finally produced by combining the rescored results. On the 2013 evaluation set, we are able to reduce the word error rate of 1.62{\%} absolute. On the 2014, provided as a development set, the word error rate of our transcription is 11.3{\%}.",
}
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<abstract>This paper describes the speech recognition system of IOIT for IWSLT 2016. Four single DNN-based systems were developed to produce the 1st-pass lattices for the test sets using a baseline language model. The 2nd-pass lattices were further obtained by applying N-best list rescoring on topic adapted language models which were constructed from closed topic sentences by applying a text selection method. The final transcriptions of test sets were finally produced by combining the rescored results. On the 2013 evaluation set, we are able to reduce the word error rate of 1.62% absolute. On the 2014, provided as a development set, the word error rate of our transcription is 11.3%.</abstract>
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%0 Conference Proceedings
%T The IOIT English ASR system for IWSLT 2016
%A Nguyen, Van Huy
%A Phung, Trung-Nghia
%A Vu, Tat Thang
%A Luong, Chi Mai
%Y Cettolo, Mauro
%Y Niehues, Jan
%Y Stüker, Sebastian
%Y Bentivogli, Luisa
%Y Cattoni, Rolando
%Y Federico, Marcello
%S Proceedings of the 13th International Conference on Spoken Language Translation
%D 2016
%8 dec 8 9
%I International Workshop on Spoken Language Translation
%C Seattle, Washington D.C
%F nguyen-etal-2016-ioit
%X This paper describes the speech recognition system of IOIT for IWSLT 2016. Four single DNN-based systems were developed to produce the 1st-pass lattices for the test sets using a baseline language model. The 2nd-pass lattices were further obtained by applying N-best list rescoring on topic adapted language models which were constructed from closed topic sentences by applying a text selection method. The final transcriptions of test sets were finally produced by combining the rescored results. On the 2013 evaluation set, we are able to reduce the word error rate of 1.62% absolute. On the 2014, provided as a development set, the word error rate of our transcription is 11.3%.
%U https://aclanthology.org/2016.iwslt-1.14
Markdown (Informal)
[The IOIT English ASR system for IWSLT 2016](https://aclanthology.org/2016.iwslt-1.14) (Nguyen et al., IWSLT 2016)
ACL
- Van Huy Nguyen, Trung-Nghia Phung, Tat Thang Vu, and Chi Mai Luong. 2016. The IOIT English ASR system for IWSLT 2016. In Proceedings of the 13th International Conference on Spoken Language Translation, Seattle, Washington D.C. International Workshop on Spoken Language Translation.