CN-Celeb: A Challenging Chinese Speaker Recognition Dataset

By Yue Fan, Jiawen Kang, Lantian Li, Kai Li, Haolin Chen, Sitong Cheng, Pengyuan Zhang, Ziya Zhou, Yunqi Cai, Dong Wang. 2019

264 citations recorded by the source corpus.

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 Vox-Celeb, 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.

Subfields: Artificial Intelligence, Evaluation & Benchmarks, Speech & Audio, Signal Processing

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Citations in the corpus

Ego4D: Around the World in 3,000 Hours of Egocentric Video (2021, background): “[64] Yue Fan, JW Kang, LT Li, KC Li, HL Chen, ST Cheng, PY Zhang, ZY Zhou, YQ Cai, and Dong Wang.”
Bayesian HMM clustering of x-vector sequences (VBx) in speaker diarization: theory, implementation and analysis on standard tasks (2020, methodology): “x-vector extractor training data The 16 kHz x-vector extractor is trained using data from VoxCeleb1 [31] (323 h of speech from 1211 speakers), VoxCeleb2 [32] (2290 h, 5994 speakers) and CN-CELEB [33] (264 h, 973 speakers).”
AISHELL-4: An Open Source Dataset for Speech Enhancement, Separation, Recognition and Speaker Diarization in Conference Scenario (2021, methodology): “The speaker embedding network 6 is based on ResNet [23] which is trained using Voxceleb1 [24], Voxceleb2 [25] and CNCeleb [26].”