# Unsupervised Cross-device Speaker Diarization And Linking Through Spatiotemporal Structure

arXiv: https://arxiv.org/abs/2610.15978 · HTML: https://mlsanity.com/paper/2610.15978/

## Key facts

- No citing papers in the map yet (data through 19 Oct 2026).
- Placed in Speaker recognition and diarization, one of 11 directions in Speech and audio language models.
- Builds on 17 of its 28 references that are in the map.
- First posted on arXiv 11 Oct 2026.

## Placed in

- Speech and audio language models / [Speaker recognition and diarization](https://mlsanity.com/speech/speaker-recognition-and-diarization/)

## Builds on (most cited first, 17 of 17)

- [ECAPA-TDNN: Emphasized Channel Attention, Propagation and Aggregation in TDNN Based Speaker Verification](https://mlsanity.com/paper/2005.07143/) (2020)
- [SpeechBrain: A General-Purpose Speech Toolkit](https://mlsanity.com/paper/2106.04624/) (2021)
- [TitaNet: Neural Model for speaker representation with 1D Depth-wise separable convolutions and global context](https://mlsanity.com/paper/2110.04410/) (2021)
- [Integrating end-to-end neural and clustering-based diarization: Getting the best of both worlds](https://mlsanity.com/paper/2010.13366/) (2020)
- [Auto-Tuning Spectral Clustering for Speaker Diarization Using Normalized Maximum Eigengap](https://mlsanity.com/paper/2003.02405/) (2020)
- [An Enhanced Res2Net with Local and Global Feature Fusion for Speaker Verification](https://mlsanity.com/paper/2305.12838/) (2023)
- [Reshape Dimensions Network for Speaker Recognition](https://mlsanity.com/paper/2407.18223/) (2024)
- [Voices Obscured in Complex Environmental Settings (VOICES) corpus](https://mlsanity.com/paper/1804.05053/) (2018)
- [3D-Speaker: A Large-Scale Multi-Device, Multi-Distance, and Multi-Dialect Corpus for Speech Representation Disentanglement](https://mlsanity.com/paper/2306.15354/) (2023)
- [The CORAL+ Algorithm for Unsupervised Domain Adaptation of PLDA](https://mlsanity.com/paper/1812.10260/) (2018)
- [The INTERSPEECH 2020 Far-Field Speaker Verification Challenge](https://mlsanity.com/paper/2005.08046/) (2020)
- [Enhancing Speaker Verification with w2v-BERT 2.0 and Knowledge Distillation guided Structured Pruning](https://mlsanity.com/paper/2510.04213/) (2025)
- [Progressive Unsupervised Person Re-identification by Tracklet Association with Spatio-Temporal Regularization](https://mlsanity.com/paper/1910.11560/) (2019)
- [Multimodal Clustering with Role Induced Constraints for Speaker Diarization](https://mlsanity.com/paper/2204.00657/) (2022)
- [ReDimNet2: Scaling Speaker Verification via Time-Pooled Dimension Reshaping](https://mlsanity.com/paper/2603.11841/) (2026)
- [Speaker Verification with Speech-Aware LLMs: Evaluation and Augmentation](https://mlsanity.com/paper/2603.10827/) (2026)
- [On Low-Bit Quantization Errors in Speaker Verification: Diagnostic and Mitigation](https://mlsanity.com/paper/2606.08078/) (2026)

## Abstract (arXiv)

Linking speakers across independently recorded rooms is difficult because microphone, room, and codec mismatch distort speaker embeddings. We study visitor linking without visitor enrollment or identity labels in simulated outpatient workflows. The proposed method combines per-device embedding centering, concurrency constraints, room-type-specific visit constraints, and a clustering threshold calibrated from concurrent visitors and identified staff voices. On simulated days built from VOiCES recordings, with companions, return visits, walk-ins, silent visits, and acoustic leakage, it improves B3-F1 over the best label-free baseline for all 11 extractors, by 5.3-14.6 points (all 95% intervals exclude zero). ECAPA-TDNN reaches 86.6, compared with 76.4 for the best label-free baseline and 87.7 for the best oracle-informed baseline. Gains persist across the reported workflow perturbations, but depend on removing duplicated cross-room speech: without this step, the method wins for only 2 of 11 extractors. Single-visit patients also remain vulnerable to false merges. These results support structure-aware linking while identifying preprocessing and identity-safety limitations that must be addressed before clinical deployment.