Implicit Self-supervised Language Representation for Spoken Language Diarization

The use of spoken language diarization (LD) as a preprocessing system might be essential in a code-switched (CS) scenario. Furthermore, implicit frameworks are preferable to explicitones, a simplicit frameworks can be easily adapted to deal with low/zero resource languages. Inspired by speaker diarization literature, three frameworks based on (a) fixed segmentation, (b) change-point-based segmentation, and (c) end-to-end (E2E) are used in this study to perform LD. The initial exploration in the constructed text-to-speech female language diarization (TTSF-LD) dataset shows, that using the x-vector as implicit language representation with appropriate analysis window length achieves, comparable performance to explicit LD. The best implicit LD performance of 6.4 in terms of Jaccard error rate (JER) is achieved by using the E2E framework. However, using the natural Microsoft CS dataset, the performance of the E2E implicit LD degrades to 60.4 JER. The performance degradation is due to the inability of the x-vector representation to capture language-specific traits. To address this shortcoming, a selfsupervised implicit language representation framework is used in this study. Compared to the x-vector representation, the selfsupervised representation yields are lative improvement of 63.9%, achieving a JER of 21.8 when used in conjunction with the E2E framework.