# Low-Latency Speech Separation Guided Diarization for Telephone Conversations

arXiv: https://arxiv.org/abs/2204.02306 · HTML: https://mlsanity.com/paper/2204.02306/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Apr 2022.

## Built on by (most cited first, 5 of 5)

- [One model to rule them all ? Towards End-to-End Joint Speaker Diarization and Speech Recognition](https://mlsanity.com/paper/2310.01688/) (2023)
- [A Study of the Scale Invariant Signal to Distortion Ratio in Speech Separation with Noisy References](https://mlsanity.com/paper/2508.14623/) (2025)
- [End-to-End Integration of Speech Separation and Voice Activity Detection for Low-Latency Diarization of Telephone Conversations](https://mlsanity.com/paper/2303.12002/) (2023)
- [Online speaker diarization of meetings guided by speech separation](https://mlsanity.com/paper/2402.00067/) (2024)
- [A Review of Common Online Speaker Diarization Methods](https://mlsanity.com/paper/2406.14464/) (2024)

## Abstract (arXiv)

In this paper, we carry out an analysis on the use of speech separation guided diarization (SSGD) in telephone conversations. SSGD performs diarization by separating the speakers signals and then applying voice activity detection on each estimated speaker signal. In particular, we compare two low-latency speech separation models. Moreover, we show a post-processing algorithm that significantly reduces the false alarm errors of a SSGD pipeline. We perform our experiments on two datasets: Fisher Corpus Part 1 and CALLHOME, evaluating both separation and diarization metrics. Notably, our SSGD DPRNN-based online model achieves 11.1% DER on CALLHOME, comparable with most state-of-the-art end-to-end neural diarization models despite being trained on an order of magnitude less data and having considerably lower latency, i.e., 0.1 vs. 10 seconds. We also show that the separated signals can be readily fed to a speech recognition back-end with performance close to the oracle source signals.