50 citations · 57 across the 18 of their papers we have counts for
11 papers · 1 filter
Multi-band Frequency Reconstruction for Neural Psychoacoustic Coding
Dianwen Ng, Kun Zhou, Yi-Wen Chao +3
Achieving high-fidelity audio compression while preserving perceptual quality across diverse content remains a key challenge in Neural Audio Coding (NAC). We introduce MUFFIN, a fu…
Towards Audio Codec-based Speech Separation
Jia Qi Yip, Shengkui Zhao, Dianwen Ng +2
Recent improvements in neural audio codec (NAC) models have generated interest in adopting pre-trained codecs for a variety of speech processing applications to take advantage of t…
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…
Are Soft Prompts Good Zero-shot Learners for Speech Recognition?
Dianwen Ng, Chong Zhang, Ruixi Zhang +7
Large self-supervised pre-trained speech models require computationally expensive fine-tuning for downstream tasks. Soft prompt tuning offers a simple parameter-efficient alternati…
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…
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,…
I2CR: Improving Noise Robustness on Keyword Spotting Using Inter-Intra Contrastive Regularization
Dianwen Ng, Jia Qi Yip, Tanmay Surana +6
Noise robustness in keyword spotting remains a challenge as many models fail to overcome the heavy influence of noises, causing the deterioration of the quality of feature embeddin…
Small Footprint Multi-channel ConvMixer for Keyword Spotting with Centroid Based Awareness
Dianwen Ng, Jin Hui Pang, Yang Xiao +3
It is critical for a keyword spotting model to have a small footprint as it typically runs on-device with low computational resources. However, maintaining the previous SOTA perfor…
ConvMixer: Feature Interactive Convolution with Curriculum Learning for Small Footprint and Noisy Far-field Keyword Spotting
Dianwen Ng, Yunqi Chen, Biao Tian +2
Building efficient architecture in neural speech processing is paramount to success in keyword spotting deployment. However, it is very challenging for lightweight models to achiev…