  Annotating a multilingual code-switched corpus is a painstaking process
requiring specialist linguistic expertise. This is partly due to the large
number of language combinations that may appear within and across utterances,
which might require several annotators with different linguistic expertise to
consider an utterance sequentially. This is time-consuming and costly. It would
be useful if the spoken languages in an utterance and the boundaries thereof
were known before annotation commences, to allow segments to be assigned to the
relevant language experts in parallel. To address this, we investigate the
development of a continuous multilingual language diarizer using fine-tuned
speech representations extracted from a large pre-trained self-supervised
architecture (WavLM). We experiment with a code-switched corpus consisting of
five 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.
