@inproceedings{melese-etal-2017-amharic,
title = "{A}mharic-{E}nglish Speech Translation in Tourism Domain",
author = "Melese, Michael and
Besacier, Laurent and
Meshesha, Million",
editor = "Ruiz, Nicholas and
Bangalore, Srinivas",
booktitle = "Proceedings of the Workshop on Speech-Centric Natural Language Processing",
month = sep,
year = "2017",
address = "Copenhagen, Denmark",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W17-4608",
doi = "10.18653/v1/W17-4608",
pages = "59--66",
abstract = "This paper describes speech translation from Amharic-to-English, particularly Automatic Speech Recognition (ASR) with post-editing feature and Amharic-English Statistical Machine Translation (SMT). ASR experiment is conducted using morpheme language model (LM) and phoneme acoustic model(AM). Likewise,SMT conducted using word and morpheme as unit. Morpheme based translation shows a 6.29 BLEU score at a 76.4{\%} of recognition accuracy while word based translation shows a 12.83 BLEU score using 77.4{\%} word recognition accuracy. Further, after post-edit on Amharic ASR using corpus based n-gram, the word recognition accuracy increased by 1.42{\%}. Since post-edit approach reduces error propagation, the word based translation accuracy improved by 0.25 (1.95{\%}) BLEU score. We are now working towards further improving propagated errors through different algorithms at each unit of speech translation cascading component.",
}
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%0 Conference Proceedings
%T Amharic-English Speech Translation in Tourism Domain
%A Melese, Michael
%A Besacier, Laurent
%A Meshesha, Million
%Y Ruiz, Nicholas
%Y Bangalore, Srinivas
%S Proceedings of the Workshop on Speech-Centric Natural Language Processing
%D 2017
%8 September
%I Association for Computational Linguistics
%C Copenhagen, Denmark
%F melese-etal-2017-amharic
%X This paper describes speech translation from Amharic-to-English, particularly Automatic Speech Recognition (ASR) with post-editing feature and Amharic-English Statistical Machine Translation (SMT). ASR experiment is conducted using morpheme language model (LM) and phoneme acoustic model(AM). Likewise,SMT conducted using word and morpheme as unit. Morpheme based translation shows a 6.29 BLEU score at a 76.4% of recognition accuracy while word based translation shows a 12.83 BLEU score using 77.4% word recognition accuracy. Further, after post-edit on Amharic ASR using corpus based n-gram, the word recognition accuracy increased by 1.42%. Since post-edit approach reduces error propagation, the word based translation accuracy improved by 0.25 (1.95%) BLEU score. We are now working towards further improving propagated errors through different algorithms at each unit of speech translation cascading component.
%R 10.18653/v1/W17-4608
%U https://aclanthology.org/W17-4608
%U https://doi.org/10.18653/v1/W17-4608
%P 59-66
Markdown (Informal)
[Amharic-English Speech Translation in Tourism Domain](https://aclanthology.org/W17-4608) (Melese et al., 2017)
ACL
- Michael Melese, Laurent Besacier, and Million Meshesha. 2017. Amharic-English Speech Translation in Tourism Domain. In Proceedings of the Workshop on Speech-Centric Natural Language Processing, pages 59–66, Copenhagen, Denmark. Association for Computational Linguistics.