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20192026
most citedSiamese Neural Network with Joint Bayesian Model Structure for Speaker Verification

2 citations · 4 across the 7 of their papers we have counts for

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6 papers · 1 filter

eess.AS2025

Cross-modal Knowledge Transfer Learning as Graph Matching Based on Optimal Transport for ASR

Xugang Lu, Peng Shen, Yu Tsao +1

Transferring linguistic knowledge from a pretrained language model (PLM) to acoustic feature learning has proven effective in enhancing end-to-end automatic speech recognition (E2E…

eess.AS2023

Neural domain alignment for spoken language recognition based on optimal transport

Xugang Lu, Peng Shen, Yu Tsao +1

Domain shift poses a significant challenge in cross-domain spoken language recognition (SLR) by reducing its effectiveness. Unsupervised domain adaptation (UDA) algorithms have bee…

eess.AS2023

Hierarchical Cross-Modality Knowledge Transfer with Sinkhorn Attention for CTC-based ASR

Xugang Lu, Peng Shen, Yu Tsao +1

Due to the modality discrepancy between textual and acoustic modeling, efficiently transferring linguistic knowledge from a pretrained language model (PLM) to acoustic encoding for…

eess.AS2023

Cross-modal Alignment with Optimal Transport for CTC-based ASR

Xugang Lu, Peng Shen, Yu Tsao +1

Temporal connectionist temporal classification (CTC)-based automatic speech recognition (ASR) is one of the most successful end to end (E2E) ASR frameworks. However, due to the tok…

eess.AS20222 cited

Partial Coupling of Optimal Transport for Spoken Language Identification

Xugang Lu, Peng Shen, Yu Tsao +1

In order to reduce domain discrepancy to improve the performance of cross-domain spoken language identification (SLID) system, as an unsupervised domain adaptation (UDA) method, we…

eess.AS20212 cited

Siamese Neural Network with Joint Bayesian Model Structure for Speaker Verification

Xugang Lu, Peng Shen, Yu Tsao +1

Generative probability models are widely used for speaker verification (SV). However, the generative models are lack of discriminative feature selection ability. As a hypothesis te…