# TSUP Speaker Diarization System for Conversational Short-phrase Speaker Diarization Challenge

arXiv: https://arxiv.org/abs/2210.14653 · HTML: https://mlsanity.com/paper/2210.14653/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 31 of its 42 references that are in the map.
- First posted on arXiv 26 Oct 2022.

## Builds on (most cited first, 30 of 31)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition](https://mlsanity.com/paper/1904.08779/) (2019)
- [VoxCeleb: a large-scale speaker identification dataset](https://mlsanity.com/paper/1706.08612/) (2017)
- [ECAPA-TDNN: Emphasized Channel Attention, Propagation and Aggregation in TDNN Based Speaker Verification](https://mlsanity.com/paper/2005.07143/) (2020)
- [MUSAN: A Music, Speech, and Noise Corpus](https://mlsanity.com/paper/1510.08484/) (2015)
- [Deep clustering: Discriminative embeddings for segmentation and separation](https://mlsanity.com/paper/1508.04306/) (2015)
- [Permutation Invariant Training of Deep Models for Speaker-Independent Multi-talker Speech Separation](https://mlsanity.com/paper/1607.00325/) (2016)
- [A Review of Speaker Diarization: Recent Advances with Deep Learning](https://mlsanity.com/paper/2101.09624/) (2021)
- [End-to-End Neural Speaker Diarization with Permutation-Free Objectives](https://mlsanity.com/paper/1909.05952/) (2019)
- [End-to-End Neural Speaker Diarization with Self-attention](https://mlsanity.com/paper/1909.06247/) (2019)
- [Speaker Diarization with LSTM](https://mlsanity.com/paper/1710.10468/) (2017)
- [M2MeT: The ICASSP 2022 Multi-Channel Multi-Party Meeting Transcription Challenge](https://mlsanity.com/paper/2110.07393/) (2021)
- [Target-Speaker Voice Activity Detection: a Novel Approach for Multi-Speaker Diarization in a Dinner Party Scenario](https://mlsanity.com/paper/2005.07272/) (2020)
- [The Third DIHARD Diarization Challenge](https://mlsanity.com/paper/2012.01477/) (2020)
- [CN-CELEB: a challenging Chinese speaker recognition dataset](https://mlsanity.com/paper/1911.01799/) (2019)
- [Open Source MagicData-RAMC: A Rich Annotated Mandarin Conversational(RAMC) Speech Dataset](https://mlsanity.com/paper/2203.16844/) (2022)
- [Auto-Tuning Spectral Clustering for Speaker Diarization Using Normalized Maximum Eigengap](https://mlsanity.com/paper/2003.02405/) (2020)
- [DOVER-Lap: A Method for Combining Overlap-aware Diarization Outputs](https://mlsanity.com/paper/2011.01997/) (2020)
- [Microsoft Speaker Diarization System for the VoxCeleb Speaker Recognition Challenge 2020](https://mlsanity.com/paper/2010.11458/) (2020)
- [LSTM based Similarity Measurement with Spectral Clustering for Speaker Diarization](https://mlsanity.com/paper/1907.10393/) (2019)
- [Neural Speaker Diarization with Speaker-Wise Chain Rule](https://mlsanity.com/paper/2006.01796/) (2020)
- [Online End-to-End Neural Diarization with Speaker-Tracing Buffer](https://mlsanity.com/paper/2006.02616/) (2020)
- [VoxSRC 2021: The Third VoxCeleb Speaker Recognition Challenge](https://mlsanity.com/paper/2201.04583/) (2022)
- [Multi-class Spectral Clustering with Overlaps for Speaker Diarization](https://mlsanity.com/paper/2011.02900/) (2020)
- [The DKU-DukeECE-Lenovo System for the Diarization Task of the 2021 VoxCeleb Speaker Recognition Challenge](https://mlsanity.com/paper/2109.02002/) (2021)
- [The ByteDance Speaker Diarization System for the VoxCeleb Speaker Recognition Challenge 2021](https://mlsanity.com/paper/2109.02047/) (2021)
- [Auxiliary Loss of Transformer with Residual Connection for End-to-End Speaker Diarization](https://mlsanity.com/paper/2110.07116/) (2021)
- [A report on sound event detection with different binaural features](https://mlsanity.com/paper/1710.02997/) (2017)
- [Cross-Channel Attention-Based Target Speaker Voice Activity Detection: Experimental Results for M2MeT Challenge](https://mlsanity.com/paper/2202.02687/) (2022)

## Built on by (most cited first, 1 of 1)

- [MLCA-AVSR: Multi-Layer Cross Attention Fusion based Audio-Visual Speech Recognition](https://mlsanity.com/paper/2401.03424/) (2024)

## Abstract (arXiv)

This paper describes the TSUP team's submission to the ISCSLP 2022 conversational short-phrase speaker diarization (CSSD) challenge which particularly focuses on short-phrase conversations with a new evaluation metric called conversational diarization error rate (CDER). In this challenge, we explore three kinds of typical speaker diarization systems, which are spectral clustering(SC) based diarization, target-speaker voice activity detection(TS-VAD) and end-to-end neural diarization(EEND) respectively. Our major findings are summarized as follows. First, the SC approach is more favored over the other two approaches under the new CDER metric. Second, tuning on hyperparameters is essential to CDER for all three types of speaker diarization systems. Specifically, CDER becomes smaller when the length of sub-segments setting longer. Finally, multi-system fusion through DOVER-LAP will worsen the CDER metric on the challenge data. Our submitted SC system eventually ranks the third place in the challenge.