Semi-supervised Development of ASR Systems for Multilingual Code-switched Speech in Under-resourced Languages

Astik Biswas, Emre Yilmaz, Febe De Wet, Ewald Van der westhuizen, Thomas Niesler


Abstract
This paper reports on the semi-supervised development of acoustic and language models for under-resourced, code-switched speech in five South African languages. Two approaches are considered. The first constructs four separate bilingual automatic speech recognisers (ASRs) corresponding to four different language pairs between which speakers switch frequently. The second uses a single, unified, five-lingual ASR system that represents all the languages (English, isiZulu, isiXhosa, Setswana and Sesotho). We evaluate the effectiveness of these two approaches when used to add additional data to our extremely sparse training sets. Results indicate that batch-wise semi-supervised training yields better results than a non-batch-wise approach. Furthermore, while the separate bilingual systems achieved better recognition performance than the unified system, they benefited more from pseudolabels generated by the five-lingual system than from those generated by the bilingual systems.
Anthology ID:
2020.lrec-1.426
Volume:
Proceedings of the 12th Language Resources and Evaluation Conference
Month:
May
Year:
2020
Address:
Marseille, France
Venues:
COLING | LREC
SIG:
Publisher:
European Language Resources Association
Note:
Pages:
3468–3474
Language:
English
URL:
https://www.aclweb.org/anthology/2020.lrec-1.426
DOI:
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PDF:
http://aclanthology.lst.uni-saarland.de/2020.lrec-1.426.pdf