15 citations · 22 across the 10 of their papers we have counts for
10 papers
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…
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…
Ditto: A Simple and Efficient Approach to Improve Sentence Embeddings
Qian Chen, Wen Wang, Qinglin Zhang +6
Prior studies diagnose the anisotropy problem in sentence representations from pre-trained language models, e.g., BERT, without fine-tuning. Our analysis reveals that the sentence…
Adaptive Knowledge Distillation between Text and Speech Pre-trained Models
Jinjie Ni, Yukun Ma, Wen Wang +7
Learning on a massive amount of speech corpus leads to the recent success of many self-supervised speech models. With knowledge distillation, these models may also benefit from 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…
A Cost-Sensitive Deep Belief Network for Imbalanced Classification
Chong Zhang, Kay Chen Tan, Haizhou Li +1
Imbalanced data with a skewed class distribution are common in many real-world applications. Deep Belief Network (DBN) is a machine learning technique that is effective in classifi…
A Multi-State Diagnosis and Prognosis Framework with Feature Learning for Tool Condition Monitoring
Chong Zhang, Geok Soon Hong, Jun-Hong Zhou +5
In this paper, a multi-state diagnosis and prognosis (MDP) framework is proposed for tool condition monitoring via a deep belief network based multi-state approach (DBNMS). For fau…