12 citations · 12 across the 5 of their papers we have counts for
6 papers
DuSpaR: Dual-State Sparsifying Recurrent Unit with Feedback Modulation for Compute-Efficient Speech Processing
Zixiao Li, Sheng Zhou, Longbiao Cheng +1
We introduce the Dual-state Sparsifying Recurrent Unit (DuSpaR) as a computationally efficient building block for speech processing models on resource-constrained edge devices. It…
When Audio-Language Models Fail to Leverage Multimodal Context for Dysarthric Speech Recognition
Pehuén Moure, Niclas Pokel, Bilal Bounajma +4
Automatic speech recognition (ASR) systems remain brittle on dysarthric and other atypical speech. Recent audio-language models raise the possibility of improving performance by co…
Towards Lightweight Adaptation of Speech Enhancement Models in Real-World Environments
Longbiao Cheng, Shih-Chii Liu
Recent studies have shown that post-deployment adaptation can improve the robustness of speech enhancement models in unseen noise conditions. However, existing methods often incur…
Modulating State Space Model with SlowFast Framework for Compute-Efficient Ultra Low-Latency Speech Enhancement
Longbiao Cheng, Ashutosh Pandey, Buye Xu +3
Deep learning-based speech enhancement (SE) methods often face significant computational challenges when needing to meet low-latency requirements because of the increased number of…
Dynamic Gated Recurrent Neural Network for Compute-efficient Speech Enhancement
Longbiao Cheng, Ashutosh Pandey, Buye Xu +2
This paper introduces a new Dynamic Gated Recurrent Neural Network (DG-RNN) for compute-efficient speech enhancement models running on resource-constrained hardware platforms. It l…
Exploiting Symmetric Temporally Sparse BPTT for Efficient RNN Training
Xi Chen, Chang Gao, Zuowen Wang +4
Recurrent Neural Networks (RNNs) are useful in temporal sequence tasks. However, training RNNs involves dense matrix multiplications which require hardware that can support a large…