# Spoken language change detection inspired by speaker change detection

arXiv: https://arxiv.org/abs/2302.05265 · HTML: https://mlsanity.com/paper/2302.05265/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Feb 2023.

## Built on by (most cited first, 3 of 3)

- [Implicit Self-supervised Language Representation for Spoken Language Diarization](https://mlsanity.com/paper/2308.10470/) (2023)
- [Implicit spoken language diarization](https://mlsanity.com/paper/2306.12913/) (2023)
- [Accurate Evaluation of Quickest Changepoint Detectors via Non-parametric Survival Analysis](https://mlsanity.com/paper/2605.18798/) (2026)

## Abstract (arXiv)

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.