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20222026
most citedEmphasized Non-Target Speaker Knowledge in Knowledge Distillation for Automatic Speaker Verification

12 citations · 22 across the 23 of their papers we have counts for

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Showing 2023 · cs.SDShow all

6 papers · 2 filters

cs.SD2023★ 1 cited

MossFormer2: Combining Transformer and RNN-Free Recurrent Network for Enhanced Time-Domain Monaural Speech Separation

Shengkui Zhao, Yukun Ma, Chongjia Ni +7

Our previously proposed MossFormer has achieved promising performance in monaural speech separation. However, it predominantly adopts a self-attention-based MossFormer module, whic…

cs.SD2023

Analysis of Speech Separation Performance Degradation on Emotional Speech Mixtures

Jia Qi Yip, Dianwen Ng, Bin Ma +1

Despite recent strides made in Speech Separation, most models are trained on datasets with neutral emotions. Emotional speech has been known to degrade performance of models in a v…

cs.SD2023★ 12 cited

Emphasized Non-Target Speaker Knowledge in Knowledge Distillation for Automatic Speaker Verification

Duc-Tuan Truong, Ruijie Tao, Jia Qi Yip +2

Knowledge distillation (KD) is used to enhance automatic speaker verification performance by ensuring consistency between large teacher networks and lightweight student networks at…

cs.SD2023

ACA-Net: Towards Lightweight Speaker Verification using Asymmetric Cross Attention

Jia Qi Yip, Tuan Truong, Dianwen Ng +7

In this paper, we propose ACA-Net, a lightweight, global context-aware speaker embedding extractor for Speaker Verification (SV) that improves upon existing work by using Asymmetri…

cs.SD2023★ 1 cited

Contrastive Speech Mixup for Low-resource Keyword Spotting

Dianwen Ng, Ruixi Zhang, Jia Qi Yip +6

Most of the existing neural-based models for keyword spotting (KWS) in smart devices require thousands of training samples to learn a decent audio representation. However, with the…

cs.SD2023

deHuBERT: Disentangling Noise in a Self-supervised Model for Robust Speech Recognition

Dianwen Ng, Ruixi Zhang, Jia Qi Yip +7

Existing self-supervised pre-trained speech models have offered an effective way to leverage massive unannotated corpora to build good automatic speech recognition (ASR). However,…