6 citations · 7 across the 5 of their papers we have counts for
6 papers
Optimizing Dysarthria Wake-Up Word Spotting: An End-to-End Approach for SLT 2024 LRDWWS Challenge
Shuiyun Liu, Yuxiang Kong, Pengcheng Guo +4
Speech has emerged as a widely embraced user interface across diverse applications. However, for individuals with dysarthria, the inherent variability in their speech poses signifi…
Understanding temporally weakly supervised training: A case study for keyword spotting
Heinrich Dinkel, Weiji Zhuang, Zhiyong Yan +3
The currently most prominent algorithm to train keyword spotting (KWS) models with deep neural networks (DNNs) requires strong supervision i.e., precise knowledge of the spoken key…
Learning Decoupling Features Through Orthogonality Regularization
Li Wang, Rongzhi Gu, Weiji Zhuang +3
Keyword spotting (KWS) and speaker verification (SV) are two important tasks in speech applications. Research shows that the state-of-art KWS and SV models are trained independentl…
Multi-channel Speech Enhancement with 2-D Convolutional Time-frequency Domain Features and a Pre-trained Acoustic Model
Quandong Wang, Junnan Wu, Zhao Yan +6
We propose a multi-channel speech enhancement approach with a novel two-stage feature fusion method and a pre-trained acoustic model in a multi-task learning paradigm. In the first…
Multi-Channel Automatic Speech Recognition Using Deep Complex Unet
Yuxiang Kong, Jian Wu, Quandong Wang +4
The front-end module in multi-channel automatic speech recognition (ASR) systems mainly use microphone array techniques to produce enhanced signals in noisy conditions with reverbe…
AutoKWS: Keyword Spotting with Differentiable Architecture Search
Bo Zhang, Wenfeng Li, Qingyuan Li +3
Smart audio devices are gated by an always-on lightweight keyword spotting program to reduce power consumption. It is however challenging to design models that have both high accur…