USING MALAY RESOURCES TO BOOTSTRAP ASR FOR A VERY UNDER-RESOURCED LANGUAGE: IBAN

Abstract : This paper describes our experiments and results on using a local dominant language in Malaysia (Malay), to boot- strap automatic speech recognition (ASR) for a very under- resourced language: Iban (also spoken in Malaysia on the Borneo Island part). Resources in Iban for building a speech recognition were nonexistent. For this, we tried to take ad- vantage of a language from the same family with several similarities. First, to deal with the pronunciation dictionary, we proposed a bootstrapping strategy to develop an Iban pronunciation lexicon from a Malay one. A hybrid version, mix of Malay and Iban pronunciations, was also built and evaluated. Following this, we experimented with three Iban ASRs; each depended on either one of the three different pronunciation dictionaries: Malay, Iban or hybrid.
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Sarah Samson Juan, Laurent Besacier, Solange Rossato. USING MALAY RESOURCES TO BOOTSTRAP ASR FOR A VERY UNDER-RESOURCED LANGUAGE: IBAN. SLTU 2014, May 2014, Saint-Petersbourg, Russia. ⟨hal-01002920⟩

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