13 citations · 126 across the 71 of their papers we have counts for
10 papers · 1 filter
A Principle Solution for Enroll-Test Mismatch in Speaker Recognition
Lantian Li, Dong Wang, Jiawen Kang +4
Mismatch between enrollment and test conditions causes serious performance degradation on speaker recognition systems. This paper presents a statistics decomposition (SD) approach…
CN-Celeb: multi-genre speaker recognition
Lantian Li, Ruiqi Liu, Jiawen Kang +6
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, enrollme…
Can We Trust Deep Speech Prior?
Ying Shi, Haolin Chen, Zhiyuan Tang +3
Recently, speech enhancement (SE) based on deep speech prior has attracted much attention, such as the variational auto-encoder with non-negative matrix factorization (VAE-NMF) arc…
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…
Squeezing value of cross-domain labels: a decoupled scoring approach for speaker verification
Lantian Li, Yang Zhang, Jiawen Kang +2
Domain mismatch often occurs in real applications and causes serious performance reduction on speaker verification systems. The common wisdom is to collect cross-domain data and tr…
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…
Neural Discriminant Analysis for Deep Speaker Embedding
Lantian Li, Dong Wang, Thomas Fang Zheng
Probabilistic Linear Discriminant Analysis (PLDA) is a popular tool in open-set classification/verification tasks. However, the Gaussian assumption underlying PLDA prevents it from…
ASR-Free Pronunciation Assessment
Sitong Cheng, Zhixin Liu, Lantian Li +3
Most of the pronunciation assessment methods are based on local features derived from automatic speech recognition (ASR), e.g., the Goodness of Pronunciation (GOP) score. In this p…
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…