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Transfer Learning for Less-Resourced Semitic Languages Speech Recognition: the Case of Amharic
Umeå University, Faculty of Science and Technology, Department of Computing Science. (Foundations of Language Processing)
2020 (English)In: Proceedings of the 1st Joint SLTU and CCURL Workshop (SLTU-CCURL 2020) / [ed] Dorothee Beermann, Laurent Besacier, Sakriani Sakti, and Claudia Soria, 2020, p. 61-69Conference paper, Published paper (Refereed)
Abstract [en]

While building automatic speech recognition (ASR) requires a large amount of speech and text data, the problem gets worse forless-resourced languages. In this paper, we investigate a model adaptation method, namely transfer learning for a less-resourced Semiticlanguage i.e., Amharic, to solve resource scarcity problems in speech recognition development and improve the Amharic ASR model. Inour experiments, we transfer acoustic models trained on two different source languages (English and Mandarin) to Amharic using verylimited resources. The experimental results show that a significant WER (Word Error Rate) reduction has been achieved by transferringthe hidden layers of the trained source languages neural networks. In the best case scenario, the Amharic ASR model adapted fromEnglish yields the best WER reduction from 38.72% to 24.50% (an improvement of 14.22% absolute). Adapting the Mandarin modelimproves the baseline Amharic model with a WER reduction of 10.25% (absolute). Our analysis also reveals that, the speech recognitionperformance of the adapted acoustic model is highly influenced by the relatedness (in a relative sense) between the source and thetarget languages than other considered factors (e.g. the quality of source models). Furthermore, other Semitic as well as Afro-Asiaticlanguages could benefit from the methodology presented in this study.

Place, publisher, year, edition, pages
2020. p. 61-69
Keywords [en]
Transfer learning, weight transfer, Automatic Speech Recognition, Less-resourced languages, Semitic languages, Amharic
National Category
Computer Sciences
Research subject
computational linguistics
Identifiers
URN: urn:nbn:se:umu:diva-170770ISBN: 979-10-95546-35-1 (print)OAI: oai:DiVA.org:umu-170770DiVA, id: diva2:1430426
Conference
Language Resources and Evaluation Conference (LREC 2020), Marseille, France 11–16 May, 2020
Available from: 2020-05-15 Created: 2020-05-15 Last updated: 2020-05-15Bibliographically approved

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Woldemariam, Yonas Demeke
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CiteExportLink to record
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Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
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