Semi-supervised G2P Bootstrapping and Its Application to ASR for a Very Under-resourced Language: Iban

Juan, Sarah Samson and Besacier, Laurent and Rossato, Solange (2014) Semi-supervised G2P Bootstrapping and Its Application to ASR for a Very Under-resourced Language: Iban. In: Proceedings of Workshop for Spoken Language Technology for Under-resourced (SLTU), St Petersbourg, Russia.

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Abstract

This paper describes our experiments and results on using a local dominant language in Malaysia (Malay), to bootstrap 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 advantage 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.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: under-resourced languages, speech recognition, Iban language, Malay language, bootstrapping, Kaldi, Universiti Malaysia Sarawak, unimas, university, universiti, Borneo, Malaysia, Sarawak, Kuching, Samarahan, ipta, education
Subjects: Q Science > Q Science (General)
Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Academic Faculties, Institutes and Centres > Faculty of Computer Science and Information Technology
Depositing User: Dr Sarah Samson Juan
Date Deposited: 16 Oct 2015 01:20
Last Modified: 16 Oct 2015 01:20
URI: http://ir.unimas.my/id/eprint/8879

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