# Microsoft Speaker Diarization System for the VoxCeleb Speaker Recognition Challenge 2020

arXiv: https://arxiv.org/abs/2010.11458 · HTML: https://mlsanity.com/paper/2010.11458/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Oct 2020.

## Built on by (most cited first, 18 of 18)

- [A Review of Speaker Diarization: Recent Advances with Deep Learning](https://mlsanity.com/paper/2101.09624/) (2021)
- [AVA-AVD: Audio-Visual Speaker Diarization in the Wild](https://mlsanity.com/paper/2111.14448/) (2021)
- [Transcribe-to-Diarize: Neural Speaker Diarization for Unlimited Number of Speakers using End-to-End Speaker-Attributed ASR](https://mlsanity.com/paper/2110.03151/) (2021)
- [Streaming Speaker-Attributed ASR with Token-Level Speaker Embeddings](https://mlsanity.com/paper/2203.16685/) (2022)
- [A Comparative Study of Modular and Joint Approaches for Speaker-Attributed ASR on Monaural Long-Form Audio](https://mlsanity.com/paper/2107.02852/) (2021)
- [The VoxCeleb Speaker Recognition Challenge: A Retrospective](https://mlsanity.com/paper/2408.14886/) (2024)
- [The Hitachi-JHU DIHARD III System: Competitive End-to-End Neural Diarization and X-Vector Clustering Systems Combined by DOVER-Lap](https://mlsanity.com/paper/2102.01363/) (2021)
- [Online Neural Diarization of Unlimited Numbers of Speakers Using Global and Local Attractors](https://mlsanity.com/paper/2206.02432/) (2022)
- [Multi-scale speaker embedding-based graph attention networks for speaker diarisation](https://mlsanity.com/paper/2110.03361/) (2021)
- [Target Speaker Voice Activity Detection with Transformers and Its Integration with End-to-End Neural Diarization](https://mlsanity.com/paper/2208.13085/) (2022)
- [In search of strong embedding extractors for speaker diarisation](https://mlsanity.com/paper/2210.14682/) (2022)
- [Leveraging Real Conversational Data for Multi-Channel Continuous Speech Separation](https://mlsanity.com/paper/2204.03232/) (2022)
- [Royalflush Speaker Diarization System for ICASSP 2022 Multi-channel Multi-party Meeting Transcription Challenge](https://mlsanity.com/paper/2202.04814/) (2022)
- [Improving Speaker Assignment in Speaker-Attributed ASR for Real Meeting Applications](https://mlsanity.com/paper/2403.06570/) (2024)
- [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)
- [SVVAD: Personal Voice Activity Detection for Speaker Verification](https://mlsanity.com/paper/2305.19581/) (2023)
- [TSUP Speaker Diarization System for Conversational Short-phrase Speaker Diarization Challenge](https://mlsanity.com/paper/2210.14653/) (2022)
- [A Teacher-Student approach for extracting informative speaker embeddings from speech mixtures](https://mlsanity.com/paper/2306.00634/) (2023)

## Abstract (arXiv)

This paper describes the Microsoft speaker diarization system for monaural multi-talker recordings in the wild, evaluated at the diarization track of the VoxCeleb Speaker Recognition Challenge(VoxSRC) 2020. We will first explain our system design to address issues in handling real multi-talker recordings. We then present the details of the components, which include Res2Net-based speaker embedding extractor, conformer-based continuous speech separation with leakage filtering, and a modified DOVER (short for Diarization Output Voting Error Reduction) method for system fusion. We evaluate the systems with the data set provided by VoxSRCchallenge 2020, which contains real-life multi-talker audio collected from YouTube. Our best system achieves 3.71% and 6.23% of the diarization error rate (DER) on development set and evaluation set, respectively, being ranked the 1st at the diarization track of the challenge.