1 citations · 2 across the 12 of their papers we have counts for
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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…
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…
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,…