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
this http URL

本文介绍了 CN-Celeb 数据集，它是一个大规模的演讲者识别数据集，收集了来自 1000 个中国名人的超过 130,000 个话语，覆盖了现实世界中的 11 个不同流派，并表明在实际情况下，现有技术的性能可能比预期的要差得多。

CN-CELEB：一个具有挑战性的中文说话人识别数据集

CN-CELEB: a challenging Chinese speaker recognition dataset

We present a face detection algorithm based on Deformable Part Models and
deep pyramidal features. The proposed method called DP2MFD is able to detect
faces of various sizes and poses in unconstrained conditions. It reduces the
gap in training and testing of DPM on deep features by adding a normalization
layer to the deep convolutional neural network (CNN). Extensive experiments on
four publicly available unconstrained face detection datasets show that our
method is able to capture the meaningful structure of faces and performs
significantly better than many competitive face detection algorithms.

提出了 DP2MFD 算法，它基于 Deformable Part Models 和深度金字塔特征来检测各种大小和姿态的人脸，通过在深度卷积神经网络上添加归一化层来减少 DPM 在深层特征训练和测试中的差距，通过在四个公开的人脸检测数据集上的广泛实验表明，该方法能够捕捉人脸的有意义的结构，并且比许多竞争性的人脸检测算法表现得更好。