12 citations · 22 across the 23 of their papers we have counts for
8 papers · 1 filter
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…
Codec Data Augmentation for Time-domain Heart Sound Classification
Ansh Mishra, Jia Qi Yip, Eng Siong Chng
Heart auscultations are a low-cost and effective way of detecting valvular heart diseases early, which can save lives. Nevertheless, it has been difficult to scale this screening m…
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…
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…
SPGM: Prioritizing Local Features for enhanced speech separation performance
Jia Qi Yip, Shengkui Zhao, Yukun Ma +8
Dual-path is a popular architecture for speech separation models (e.g. Sepformer) which splits long sequences into overlapping chunks for its intra- and inter-blocks that separatel…
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…
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…
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,…