# SAGE-LD: Towards Scalable and Generalizable End-to-End Language Diarization via Simulated Data Augmentation

arXiv: https://arxiv.org/abs/2510.00582 · HTML: https://mlsanity.com/paper/2510.00582/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 20 of its 28 references that are in the map.
- First posted on arXiv 1 Oct 2025.

## Builds on (most cited first, 20 of 20)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
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- [Conformer: Convolution-augmented Transformer for Speech Recognition](https://mlsanity.com/paper/2005.08100/) (2020)
- [XLS-R: Self-supervised Cross-lingual Speech Representation Learning at Scale](https://mlsanity.com/paper/2111.09296/) (2021)
- [Scaling Speech Technology to 1,000+ Languages](https://mlsanity.com/paper/2305.13516/) (2023)
- [Layer-wise Analysis of a Self-supervised Speech Representation Model](https://mlsanity.com/paper/2107.04734/) (2021)
- [A Novel Focal Tversky loss function with improved Attention U-Net for lesion segmentation](https://mlsanity.com/paper/1810.07842/) (2018)
- [Voice Conversion With Just Nearest Neighbors](https://mlsanity.com/paper/2305.18975/) (2023)
- [Building and Evaluation of a Real Room Impulse Response Dataset](https://mlsanity.com/paper/1811.06795/) (2018)
- [Zero-shot Voice Conversion with Diffusion Transformers](https://mlsanity.com/paper/2411.09943/) (2024)
- [Reducing Language confusion for Code-switching Speech Recognition with Token-level Language Diarization](https://mlsanity.com/paper/2210.14567/) (2022)
- [A Survey of Multilingual Models for Automatic Speech Recognition](https://mlsanity.com/paper/2202.12576/) (2022)
- [FLEURS-R: A Restored Multilingual Speech Corpus for Generation Tasks](https://mlsanity.com/paper/2408.06227/) (2024)
- [MERLIon CCS Challenge: A English-Mandarin code-switching child-directed speech corpus for language identification and diarization](https://mlsanity.com/paper/2305.18881/) (2023)
- [The Second DISPLACE Challenge : DIarization of SPeaker and LAnguage in Conversational Environments](https://mlsanity.com/paper/2406.09494/) (2024)
- [Fine-Tuned Self-Supervised Speech Representations for Language Diarization in Multilingual Code-Switched Speech](https://mlsanity.com/paper/2312.09645/) (2023)
- [EEND-M2F: Masked-attention mask transformers for speaker diarization](https://mlsanity.com/paper/2401.12600/) (2024)
- [TalTech-IRIT-LIS Speaker and Language Diarization Systems for DISPLACE 2024](https://mlsanity.com/paper/2407.12743/) (2024)
- [UniCoM: A Universal Code-Switching Speech Generator](https://mlsanity.com/paper/2508.15244/) (2025)

## Abstract (arXiv)

In this paper, we present a neural spoken language diarization model that supports an unconstrained span of languages within a single framework. Our approach integrates a learnable query-based architecture grounded in multilingual awareness, with large-scale pretraining on simulated code-switching data. By jointly leveraging these two components, our method overcomes the limitations of conventional approaches in data scarcity and architecture optimization, and generalizes effectively to real-world multilingual settings across diverse environments. Experimental results demonstrate that our approach achieves state-of-the-art performance on several language diarization benchmarks, with a relative performance improvement of 23% to 52% over previous methods. We believe that this work not only advances research in language diarization but also establishes a foundational framework for code-switching speech technologies.