most citedT-vectors: Weakly Supervised Speaker Identification Using Hierarchical Transformer Model

6 citations · 9 across the 4 of their papers we have counts for

collaborators

9 papers

cs.SD20206 cited

T-vectors: Weakly Supervised Speaker Identification Using Hierarchical Transformer Model

Yanpei Shi, Mingjie Chen, Qiang Huang +1

Identifying multiple speakers without knowing where a speaker's voice is in a recording is a challenging task. This paper proposes a hierarchical network with transformer encoders…

cs.SD2020

Towards Low-Resource StarGAN Voice Conversion using Weight Adaptive Instance Normalization

Mingjie Chen, Yanpei Shi, Thomas Hain

Many-to-many voice conversion with non-parallel training data has seen significant progress in recent years. StarGAN-based models have been interests of voice conversion. However,…

eess.AS20201 cited

Speaker Re-identification with Speaker Dependent Speech Enhancement

Yanpei Shi, Qiang Huang, Thomas Hain

While the use of deep neural networks has significantly boosted speaker recognition performance, it is still challenging to separate speakers in poor acoustic environments. Here sp…

eess.AS20201 cited

Weakly Supervised Training of Hierarchical Attention Networks for Speaker Identification

Yanpei Shi, Qiang Huang, Thomas Hain

Identifying multiple speakers without knowing where a speaker's voice is in a recording is a challenging task. In this paper, a hierarchical attention network is proposed to solve…

cs.SD2020

Supervised Speaker Embedding De-Mixing in Two-Speaker Environment

Yanpei Shi, Thomas Hain

Separating different speaker properties from a multi-speaker environment is challenging. Instead of separating a two-speaker signal in signal space like speech source separation, a…

cs.CL2020

Robust Speaker Recognition Using Speech Enhancement And Attention Model

Yanpei Shi, Qiang Huang, Thomas Hain

In this paper, a novel architecture for speaker recognition is proposed by cascading speech enhancement and speaker processing. Its aim is to improve speaker recognition performanc…