@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://preview.aclanthology.org/add-emnlp-2024-awards/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."
}