13 citations · 22 across the 6 of their papers we have counts for
7 papers
Deep Speaker Vector Normalization with Maximum Gaussianality Training
Yunqi Cai, Lantian Li, Dong Wang +1
Deep speaker embedding represents the state-of-the-art technique for speaker recognition. A key problem with this approach is that the resulting deep speaker vectors tend to be irr…
Deep generative LDA
Yunqi Cai, Dong Wang
Linear discriminant analysis (LDA) is a popular tool for classification and dimension reduction. Limited by its linear form and the underlying Gaussian assumption, however, LDA is…
Deep generative factorization for speech signal
Haoran Sun, Lantian Li, Yunqi Cai +3
Various information factors are blended in speech signals, which forms the primary difficulty for most speech information processing tasks. An intuitive idea is to factorize speech…
Domain-Invariant Speaker Vector Projection by Model-Agnostic Meta-Learning
Jiawen Kang, Ruiqi Liu, Lantian Li +3
Domain generalization remains a critical problem for speaker recognition, even with the state-of-the-art architectures based on deep neural nets. For example, a model trained on re…
Deep Normalization for Speaker Vectors
Yunqi Cai, Lantian Li, Dong Wang +1
Deep speaker embedding has demonstrated state-of-the-art performance in speaker recognition tasks. However, one potential issue with this approach is that the speaker vectors deriv…
CN-CELEB: a challenging Chinese speaker recognition dataset
Yue Fan, Jiawen Kang, Lantian Li +7
Recently, researchers set an ambitious goal of conducting speaker recognition in unconstrained conditions where the variations on ambient, channel and emotion could be arbitrary. H…