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dc.contributor.authorDe Villiers, Pieter
dc.contributor.authorBarnard, Etienne
dc.contributor.authorVan Heerden, Charl
dc.identifier.citationDe Villiers, P.T. 2014. Semi-supervised training for lecture transcription in resource-scarce environments. (In: Proceedings of the 2014 PRASA, RobMech and AfLaT International Joint Symposium, Cape Town, South Africa, 27-28 November 2014. p. 7-12).en_US
dc.description.abstractWe present a study where standard semi-supervised training methods are applied in a resource-scarce environment to build lecture transcription systems. Experiments are conducted on two different corpora which one can expect to be available in resource-scarce environments. These include 1) speaker- and domain-specific data where a single South African English lecturer presents the “Operating Systems” course, and 2) Afrikaans speaker-independent and domain non-specific data collected from science and law courses. Different amounts of acoustic and language model data are used for training the respective models. We find that lecture transcription systems in resource-scarce environments can benefit substantially from semi-supervised training methods. We also describe a small, new corpus of spoken lectures which is freely available in the public domain.en_US
dc.subjectLecture transcriptionen_US
dc.subjectLanguage modelen_US
dc.titleSemi-supervised training for lecture transcription in resource-scarce environmentsen_US
dc.contributor.researchID21281858 - De Villiers, Pieter Theunis
dc.contributor.researchID21021287 - Barnard, Etienne
dc.contributor.researchID11539151 - Van Heerden, Carel Jacobus

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