Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
485434 | Procedia Computer Science | 2016 | 8 Pages |
Abstract
The official languages of South Africa can still be classified as under-resourced with respect to the speech resources that are required for technology development. Harvesting speech data from existing sources is one means to create additional resources. The aim of the study reported on in this paper was to improve the harvesting and transcription accuracy of a corpus derived from parliamentary data. This aim was achieved by improving on the text normalisation process and pronunciation modelling as well as by iteratively training more accurate in-domain acoustic models. In this manner, more data could be harvested with higher confidence than using baseline pronunciation dictionaries and out-of-domain speech data.
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Physical Sciences and Engineering
Computer Science
Computer Science (General)
Authors
Febe de Wet, Jaco Badenhorst, Thipe Modipa,