13 citations · 126 across the 71 of their papers we have counts for
4 papers · 1 filter
Max-margin Metric Learning for Speaker Recognition
Lantian Li, Dong Wang, Chao Xing +1
Probabilistic linear discriminant analysis (PLDA) is a popular normalization approach for the i-vector model, and has delivered state-of-the-art performance in speaker recognition.…
Binary Speaker Embedding
Lantian Li, Dong Wang, Chao Xing +2
The popular i-vector model represents speakers as low-dimensional continuous vectors (i-vectors), and hence it is a way of continuous speaker embedding. In this paper, we investiga…
Improved Deep Speaker Feature Learning for Text-Dependent Speaker Recognition
Lantian Li, Yiye Lin, Zhiyong Zhang +1
A deep learning approach has been proposed recently to derive speaker identifies (d-vector) by a deep neural network (DNN). This approach has been applied to text-dependent speaker…
Deep Speaker Vectors for Semi Text-independent Speaker Verification
Lantian Li, Dong Wang, Zhiyong Zhang +1
Recent research shows that deep neural networks (DNNs) can be used to extract deep speaker vectors (d-vectors) that preserve speaker characteristics and can be used in speaker veri…