# CN-Celeb: multi-genre speaker recognition

arXiv: https://arxiv.org/abs/2012.12468 · HTML: https://mlsanity.com/paper/2012.12468/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Dec 2020.

## Built on by (most cited first, 27 of 27)

- [AnyoneNet: Synchronized Speech and Talking Head Generation for Arbitrary Person](https://mlsanity.com/paper/2108.04325/) (2021)
- [Domain Generalization with Relaxed Instance Frequency-wise Normalization for Multi-device Acoustic Scene Classification](https://mlsanity.com/paper/2206.12513/) (2022)
- [VoxSRC 2022: The Fourth VoxCeleb Speaker Recognition Challenge](https://mlsanity.com/paper/2302.10248/) (2023)
- [The VoxCeleb Speaker Recognition Challenge: A Retrospective](https://mlsanity.com/paper/2408.14886/) (2024)
- [Domain Agnostic Few-shot Learning for Speaker Verification](https://mlsanity.com/paper/2206.13700/) (2022)
- [CN-Celeb-AV: A Multi-Genre Audio-Visual Dataset for Person Recognition](https://mlsanity.com/paper/2305.16049/) (2023)
- [Real Additive Margin Softmax for Speaker Verification](https://mlsanity.com/paper/2110.09116/) (2021)
- [The Volcspeech system for the ICASSP 2022 multi-channel multi-party meeting transcription challenge](https://mlsanity.com/paper/2202.04261/) (2022)
- [Royalflush Speaker Diarization System for ICASSP 2022 Multi-channel Multi-party Meeting Transcription Challenge](https://mlsanity.com/paper/2202.04814/) (2022)
- [The Conversational Short-phrase Speaker Diarization (CSSD) Task: Dataset, Evaluation Metric and Baselines](https://mlsanity.com/paper/2208.08042/) (2022)
- [VoxVietnam: a Large-Scale Multi-Genre Dataset for Vietnamese Speaker Recognition](https://mlsanity.com/paper/2501.00328/) (2024)
- [Enhancing Speaker Verification with w2v-BERT 2.0 and Knowledge Distillation guided Structured Pruning](https://mlsanity.com/paper/2510.04213/) (2025)
- [TMS: A Temporal Multi-scale Backbone Design for Speaker Embedding](https://mlsanity.com/paper/2203.09098/) (2022)
- [CoReLoop: Parameter-Efficient Controlled Recurrent Refinement for Audio Deepfake Detection](https://mlsanity.com/paper/2609.19818/) (2026)
- [Tackling Spoofing-Aware Speaker Verification with Multi-Model Fusion](https://mlsanity.com/paper/2206.09131/) (2022)
- [Label-free Knowledge Distillation with Contrastive Loss for Light-weight Speaker Recognition](https://mlsanity.com/paper/2212.03090/) (2022)
- [Multi-Accent Mandarin Dry-Vocal Singing Dataset: Benchmark for Singing Accent Recognition](https://mlsanity.com/paper/2512.07005/) (2025)
- [Hyperbolic Additive Margin Softmax with Hierarchical Information for Speaker Verification](https://mlsanity.com/paper/2601.19709/) (2026)
- [The SpeakIn System Description for CNSRC2022](https://mlsanity.com/paper/2209.10846/) (2022)
- [Cluster-Guided Unsupervised Domain Adaptation for Deep Speaker Embedding](https://mlsanity.com/paper/2303.15944/) (2023)
- [Progressive Sub-Graph Clustering Algorithm for Semi-Supervised Domain Adaptation Speaker Verification](https://mlsanity.com/paper/2305.12703/) (2023)
- [Ordered and Binary Speaker Embedding](https://mlsanity.com/paper/2305.16043/) (2023)
- [DASA: Difficulty-Aware Semantic Augmentation for Speaker Verification](https://mlsanity.com/paper/2310.12111/) (2023)
- [Seewo's Submission to MLC-SLM: Lessons learned from Speech Reasoning Language Models](https://mlsanity.com/paper/2506.13300/) (2025)
- [Adaptive Regularization for Sparsity Control in Bregman-Based Optimizers](https://mlsanity.com/paper/2605.07892/) (2026)
- [Kiwano: A Cutting-Edge Open-Source Toolkit for Speaker Verification](https://mlsanity.com/paper/2606.22369/) (2026)
- [VieSpeaker: A Large-Scale Vietnamese Speaker Recognition Dataset Beyond Visual Dependency](https://mlsanity.com/paper/2606.24066/) (2026)

## Abstract (arXiv)

Research on speaker recognition is extending to address the vulnerability in the wild conditions, among which genre mismatch is perhaps the most challenging, for instance, enrollment with reading speech while testing with conversational or singing audio. This mismatch leads to complex and composite inter-session variations, both intrinsic (i.e., speaking style, physiological status) and extrinsic (i.e., recording device, background noise). Unfortunately, the few existing multi-genre corpora are not only limited in size but are also recorded under controlled conditions, which cannot support conclusive research on the multi-genre problem. In this work, we firstly publish CN-Celeb, a large-scale multi-genre corpus that includes in-the-wild speech utterances of 3,000 speakers in 11 different genres. Secondly, using this dataset, we conduct a comprehensive study on the multi-genre phenomenon, in particular the impact of the multi-genre challenge on speaker recognition and the performance gain when the new dataset is used to conduct multi-genre training.