Synthesising isiZulu-English code-switch bigrams using word embeddings

Code-switching is prevalent among South African speakers, and presents a challenge to automatic speech recognition systems. It is predominantly a spoken phenomenon. and generally does not occur in textual form. Therefore a particularly serious challenge is the extreme lack of training material for language modelling. We investigate the use of word embeddings to synthesise isiZulu-to-English code-switch bigrams with which to augment such sparse language model training data. A variety of word embeddings are trained on a monolingual English web text corpus, and subsequently queried to synthesise code-switch bigrams. Our evaluation is performed on language models trained on a new, although small. English-isiZulu code switch corpus compiled from South African soap operas. This data is characterised by fast. spontaneously spoken speech containing frequent code-switching. We show that the augmentation of the training data with code-switched bigrams synthesised in this way leads to a reduction in perplexity.