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20152022
most citedKnowledge Transfer Pre-training

14 citations · 108 across the 30 of their papers we have counts for

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12 papers · 1 filter

eess.AS20211 cited

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…

eess.AS2020

AP20-OLR Challenge: Three Tasks and Their Baselines

Zheng Li, Miao Zhao, Qingyang Hong +5

This paper introduces the fifth oriental language recognition (OLR) challenge AP20-OLR, which intends to improve the performance of language recognition systems, along with APSIPA…

eess.AS20201 cited

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…

eess.AS20202 cited

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…

eess.AS20203 cited

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…

eess.AS2020

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…