  Spoken language change detection (LCD) refers to identifying the language
transitions in a code-switched utterance. Similarly, identifying the speaker
transitions in a multispeaker utterance is known as speaker change detection
(SCD). Since tasks-wise both are similar, the architecture/framework developed
for the SCD task may be suitable for the LCD task. Hence, the aim of the
present work is to develop LCD systems inspired by SCD. Initially, both LCD and
SCD are performed by humans. The study suggests humans require (a) a larger
duration around the change point and (b) language-specific prior exposure, for
performing LCD as compared to SCD. The larger duration requirement is
incorporated by increasing the analysis window length of the unsupervised
distance-based approach. This leads to a relative performance improvement of
29.1% and 2.4%, and a priori language knowledge provides a relative improvement
of 31.63% and 14.27% on the synthetic and practical codeswitched datasets,
respectively. The performance difference between the practical and synthetic
datasets is mostly due to differences in the distribution of the monolingual
segment duration.
