# CN-CELEB: a challenging Chinese speaker recognition dataset

arXiv: https://arxiv.org/abs/1911.01799 · HTML: https://mlsanity.com/paper/1911.01799/

## Key facts

- Cited by 48 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Oct 2019.

## Built on by (most cited first, 30 of 48)

- [Ego4D: Around the World in 3,000 Hours of Egocentric Video](https://mlsanity.com/paper/2110.07058/) (2021)
- [CVSS Corpus and Massively Multilingual Speech-to-Speech Translation](https://mlsanity.com/paper/2201.03713/) (2022)
- [Open Source MagicData-RAMC: A Rich Annotated Mandarin Conversational(RAMC) Speech Dataset](https://mlsanity.com/paper/2203.16844/) (2022)
- [Towards Holistic Evaluation of Large Audio-Language Models: A Comprehensive Survey](https://mlsanity.com/paper/2505.15957/) (2025)
- [Turn-to-Diarize: Online Speaker Diarization Constrained by Transformer Transducer Speaker Turn Detection](https://mlsanity.com/paper/2109.11641/) (2021)
- [UniCon: Unified Context Network for Robust Active Speaker Detection](https://mlsanity.com/paper/2108.02607/) (2021)
- [Speech Self-Supervised Representations Benchmarking: a Case for Larger Probing Heads](https://mlsanity.com/paper/2308.14456/) (2023)
- [Domain Generalization with Relaxed Instance Frequency-wise Normalization for Multi-device Acoustic Scene Classification](https://mlsanity.com/paper/2206.12513/) (2022)
- [3D-Speaker: A Large-Scale Multi-Device, Multi-Distance, and Multi-Dialect Corpus for Speech Representation Disentanglement](https://mlsanity.com/paper/2306.15354/) (2023)
- [Highly Efficient Real-Time Streaming and Fully On-Device Speaker Diarization with Multi-Stage Clustering](https://mlsanity.com/paper/2210.13690/) (2022)
- [Personalized Keyphrase Detection using Speaker and Environment Information](https://mlsanity.com/paper/2104.13970/) (2021)
- [Covo-Audio Technical Report](https://mlsanity.com/paper/2602.09823/) (2026)
- [Multi-user VoiceFilter-Lite via Attentive Speaker Embedding](https://mlsanity.com/paper/2107.01201/) (2021)
- [A Multi-Purpose Audio-Visual Corpus for Multi-Modal Persian Speech Recognition: the Arman-AV Dataset](https://mlsanity.com/paper/2301.10180/) (2023)
- [CN-Celeb-AV: A Multi-Genre Audio-Visual Dataset for Person Recognition](https://mlsanity.com/paper/2305.16049/) (2023)
- [Exploring Speech Foundation Models for Speaker Diarization in Child-Adult Dyadic Interactions](https://mlsanity.com/paper/2406.07890/) (2024)
- [The Volcspeech system for the ICASSP 2022 multi-channel multi-party meeting transcription challenge](https://mlsanity.com/paper/2202.04261/) (2022)
- [DT-SV: A Transformer-based Time-domain Approach for Speaker Verification](https://mlsanity.com/paper/2205.13249/) (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)
- [Parameter-Free Attentive Scoring for Speaker Verification](https://mlsanity.com/paper/2203.05642/) (2022)
- [TMS: A Temporal Multi-scale Backbone Design for Speaker Embedding](https://mlsanity.com/paper/2203.09098/) (2022)
- [Speaker Embedding-aware Neural Diarization: an Efficient Framework for Overlapping Speech Diarization in Meeting Scenarios](https://mlsanity.com/paper/2203.09767/) (2022)
- [Robust Channel Learning for Large-Scale Radio Speaker Verification](https://mlsanity.com/paper/2406.10956/) (2024)
- [Cross-Modal Distillation For Widely Differing Modalities](https://mlsanity.com/paper/2507.16296/) (2025)
- [Scores Know Bobs Voice: Speaker Impersonation Attack](https://mlsanity.com/paper/2603.02781/) (2026)
- [Tackling Spoofing-Aware Speaker Verification with Multi-Model Fusion](https://mlsanity.com/paper/2206.09131/) (2022)
- [TSUP Speaker Diarization System for Conversational Short-phrase Speaker Diarization Challenge](https://mlsanity.com/paper/2210.14653/) (2022)
- [Adversarial Data Augmentation for Robust Speaker Verification](https://mlsanity.com/paper/2402.02699/) (2024)
- [A Semi-Automatic Approach to Create Large Gender- and Age-Balanced Speaker Corpora: Usefulness of Speaker Diarization & Identification](https://mlsanity.com/paper/2404.17552/) (2024)

## Abstract (arXiv)

Recently, researchers set an ambitious goal of conducting speaker recognition in unconstrained conditions where the variations on ambient, channel and emotion could be arbitrary. However, most publicly available datasets are collected under constrained environments, i.e., with little noise and limited channel variation. These datasets tend to deliver over optimistic performance and do not meet the request of research on speaker recognition in unconstrained conditions. In this paper, we present CN-Celeb, a large-scale speaker recognition dataset collected `in the wild'. This dataset contains more than 130,000 utterances from 1,000 Chinese celebrities, and covers 11 different genres in real world. Experiments conducted with two state-of-the-art speaker recognition approaches (i-vector and x-vector) show that the performance on CN-Celeb is far inferior to the one obtained on VoxCeleb, a widely used speaker recognition dataset. This result demonstrates that in real-life conditions, the performance of existing techniques might be much worse than it was thought. Our database is free for researchers and can be downloaded from http://project.cslt.org.