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Annotating a multilingual code-switched corpus is a painstaking processrequiring specialist linguistic expertise. This is partly due to the largenumber of language combinations that may appear within and across utterances,which might require several annotators with different linguistic expertise toconsider an utterance sequentially. This is time-consuming and costly. It wouldbe useful if the spoken languages in an utterance and the boundaries thereofwere known before annotation commences, to allow segments to be assigned to therelevant language experts in parallel. To address this, we investigate thedevelopment of a continuous multilingual language diarizer using fine-tunedspeech representations extracted from a large pre-trained self-supervisedarchitecture (WavLM). We experiment with a code-switched corpus consisting offive South African languages (isiZulu, isiXhosa, Setswana, Sesotho and English)and show substantial diarization error rate improvements for language families,language groups, and individual languages over baseline systems.

Fine-Tuned Self-Supervised Speech Representations for Language Diarization in Multilingual Code-Switched Speech