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Spoken language change detection (LCD) refers to identifying the languagetransitions in a code-switched utterance. Similarly, identifying the speakertransitions in a multispeaker utterance is known as speaker change detection(SCD). Since tasks-wise both are similar, the architecture/framework developedfor the SCD task may be suitable for the LCD task. Hence, the aim of thepresent work is to develop LCD systems inspired by SCD. Initially, both LCD andSCD are performed by humans. The study suggests humans require (a) a largerduration around the change point and (b) language-specific prior exposure, forperforming LCD as compared to SCD. The larger duration requirement isincorporated by increasing the analysis window length of the unsuperviseddistance-based approach. This leads to a relative performance improvement of29.1% and 2.4%, and a priori language knowledge provides a relative improvementof 31.63% and 14.27% on the synthetic and practical codeswitched datasets,respectively. The performance difference between the practical and syntheticdatasets is mostly due to differences in the distribution of the monolingualsegment duration.

Spoken language change detection inspired by speaker change detection