Papers by Binyam Ephrem Seyoum
Large Vocabulary Read Speech Corpora for Four Ethiopian Languages: Amharic, Tigrigna, Oromo and Wolaytta (2020.lrec-1)
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Solomon Teferra Abate, Martha Yifiru Tachbelie, Michael Melese, Hafte Abera, Tewodros Abebe, Wondwossen Mulugeta, Yaregal Assabie, Million Meshesha, Solomon Afnafu, Binyam Ephrem Seyoum
| Challenge: | Automatic Speech Recognition (ASR) is one of the most important technologies to support spoken communication in modern life. |
| Approach: | They have developed four large speech corpora for four Ethiopian languages . they have word error rates of 37.65%, 31.03%, 38.02%, 33.89% for each language . |
| Outcome: | The proposed corpora achieve word error rates of 37.65%, 31.03%, 38.02%, 33.89% for Amharic, Tigrigna, Oromo and Wolaytta. |
Universal Dependencies for Amharic (L18-1)
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| Challenge: | Amharic is a morphologically rich language with a dependency relation between orthographic words and lexical categories. |
| Approach: | They propose to create an Amharic Dependency Treebank by POS tagging, morphological information and dependency relations. |
| Outcome: | The proposed treebanks are based on 1,096 sentences and are able to parse Amharic. |
Portable Spelling Corrector for a Less-Resourced Language: Amharic (L18-1)
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| Challenge: | a corpus-driven spelling corrector for Amharic is ported to other languages with little effort . a term list is used for spelling errors and can handle rare terms, proper nouns and neologisms. |
| Approach: | They propose an automatic spelling corrector for Amharic, the working language of the Federal Government of Ethiopia. |
| Outcome: | The proposed method outperforms baseline systems in Amharic and English . it has smoothed language model, generalized error model and ability to take into account context of misspellings. |