1 code implementation • 22 Sep 2023 • Jia Qi Yip, Shengkui Zhao, Yukun Ma, Chongjia Ni, Chong Zhang, Hao Wang, Trung Hieu Nguyen, Kun Zhou, Dianwen Ng, Eng Siong Chng, Bin Ma
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 separately model intra-chunk local features and inter-chunk global relationships.
Ranked #5 on Speech Separation on WSJ0-2mix
1 code implementation • 20 May 2023 • Jia Qi Yip, Tuan Truong, Dianwen Ng, Chong Zhang, Yukun Ma, Trung Hieu Nguyen, Chongjia Ni, Shengkui Zhao, Eng Siong Chng, Bin Ma
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 Asymmetric Cross Attention (ACA) to replace temporal pooling.
no code implementations • 7 Mar 2023 • Jinjie Ni, Yukun Ma, Wen Wang, Qian Chen, Dianwen Ng, Han Lei, Trung Hieu Nguyen, Chong Zhang, Bin Ma, Erik Cambria
Learning on a massive amount of speech corpus leads to the recent success of many self-supervised speech models.
no code implementations • 1 Aug 2022 • Jia Qi Yip, Dianwen Ng, Bin Ma, Konstantin Pervushin, Eng Siong Chng
Nuclear Magnetic Resonance (NMR) is used in structural biology to experimentally determine the structure of proteins, which is used in many areas of biology and is an important part of drug development.
no code implementations • 26 Jan 2022 • Zhao Yang, Dianwen Ng, Xiao Fu, Liping Han, Wei Xi, Rui Wang, Rui Jiang, Jizhong Zhao
Based on the above intuition, we first investigate types of end-to-end encoder-decoder based models in the single-input dual-output (SIDO) multi-task framework, after which a novel asynchronous decoding with fuzzy Pinyin sampling method is proposed according to the one-to-one correspondence characteristics between Pinyin and Character.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +3
no code implementations • 18 Sep 2021 • Xiang Lan, Dianwen Ng, Shenda Hong, Mengling Feng
In inter subject self-supervision, we design a set of data augmentations according to the clinical characteristics of cardiac signals and perform contrastive learning among subjects to learn distinctive representations for various types of patients.