# Streaming Sortformer: Speaker Cache-Based Online Speaker Diarization with Arrival-Time Ordering

arXiv: https://arxiv.org/abs/2507.18446 · HTML: https://mlsanity.com/paper/2507.18446/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Jul 2025.

## Built on by (most cited first, 8 of 8)

- [Benchmarking Diarization Models](https://mlsanity.com/paper/2509.26177/) (2025)
- [GLAD: Global-Local Aware Dynamic Mixture-of-Experts for Multi-Talker ASR](https://mlsanity.com/paper/2509.13093/) (2025)
- [G-STAR: End-to-End Global Speaker-Tracking Attributed Recognition](https://mlsanity.com/paper/2603.10468/) (2026)
- [Efficiency-Performance Trade-offs in Neural Speaker Diarization via Structured Pruning and Low-Bit Quantization](https://mlsanity.com/paper/2606.14030/) (2026)
- [FASTDIAR: Frame-level speaker encoder for Streaming Diarization](https://mlsanity.com/paper/2610.02941/) (2026)
- [When Forgetting Looks Like Improvement: Metric Masking in Streaming Diarizer Adaptation and the Price of Rehearsal](https://mlsanity.com/paper/2610.08828/) (2026)
- [CAST-ASD: Causal Attention over Speakers and Time for Online Active Speaker Detection](https://mlsanity.com/paper/2610.14676/) (2026)
- [Anuvani-Diarization: Zero Shot Speaker Diarization and Annotation Conventions Across 22 Languages](https://mlsanity.com/paper/2610.20327/) (2026)

## Abstract (arXiv)

This paper presents a streaming extension for the Sortformer speaker diarization framework, whose key property is the arrival-time ordering of output speakers. The proposed approach employs an Arrival-Order Speaker Cache (AOSC) to store frame-level acoustic embeddings of previously observed speakers. Unlike conventional speaker-tracing buffers, AOSC orders embeddings by speaker index corresponding to their arrival time order, and is dynamically updated by selecting frames with the highest scores based on the model's past predictions. Notably, the number of stored embeddings per speaker is determined dynamically by the update mechanism, ensuring efficient cache utilization and precise speaker tracking. Experiments on benchmark datasets confirm the effectiveness and flexibility of our approach, even in low-latency setups. These results establish Streaming Sortformer as a robust solution for real-time multi-speaker tracking and a foundation for streaming multi-talker speech processing.