  Spoken language diarization (LD) and related tasks are mostly explored using
the phonotactic approach. Phonotactic approaches mostly use explicit way of
language modeling, hence requiring intermediate phoneme modeling and
transcribed data. Alternatively, the ability of deep learning approaches to
model temporal dynamics may help for the implicit modeling of language
information through deep embedding vectors. Hence this work initially explores
the available speaker diarization frameworks that capture speaker information
implicitly to perform LD tasks. The performance of the LD system on synthetic
code-switch data using the end-to-end x-vector approach is 6.78% and 7.06%, and
for practical data is 22.50% and 60.38%, in terms of diarization error rate and
Jaccard error rate (JER), respectively. The performance degradation is due to
the data imbalance and resolved to some extent by using pre-trained wave2vec
embeddings that provide a relative improvement of 30.74% in terms of JER.
