13 citations · 71 across the 25 of their papers we have counts for
30 papers
Pay Attention to Hard Trials
Lantian Li, Di Wang, Dong Wang
Performance of speaker recognition systems is evaluated on test trials. Although as crucial as rulers for tailors, trials have not been carefully treated so far, and most existing…
Reliable Visualization for Deep Speaker Recognition
Pengqi Li, Lantian Li, Askar Hamdulla +1
In spite of the impressive success of convolutional neural networks (CNNs) in speaker recognition, our understanding to CNNs' internal functions is still limited. A major obstacle…
Enhanced exemplar autoencoder with cycle consistency loss in any-to-one voice conversion
Weida Liang, Lantian Li, Wenqiang Du +1
Recent research showed that an autoencoder trained with speech of a single speaker, called exemplar autoencoder (eAE), can be used for any-to-one voice conversion (VC). Compared to…
C-P Map: A Novel Evaluation Toolkit for Speaker Verification
Lantian Li, Di Wang, Wenqiang Du +1
Evaluation trials are used to probe performance of automatic speaker verification (ASV) systems. In spite of the clear importance and impact, evaluation trials have not been seriou…
CycleFlow: Purify Information Factors by Cycle Loss
Haoran Sun, Chen Chen, Lantian Li +1
SpeechFlow is a powerful factorization model based on information bottleneck (IB), and its effectiveness has been reported by several studies. A potential problem of SpeechFlow, ho…
Real Additive Margin Softmax for Speaker Verification
Lantian Li, Ruiqian Nai, Dong Wang
The additive margin softmax (AM-Softmax) loss has delivered remarkable performance in speaker verification. A supposed behavior of AM-Softmax is that it can shrink within-class var…