126 citations · 244 across the 60 of their papers we have counts for
13 papers · 1 filter
Enhancing Noise Robustness for Neural Speech Codecs through Resource-Efficient Progressive Quantization Perturbation Simulation
Rui-Chen Zheng, Yang Ai, Hui-Peng Du +1
Noise robustness remains a critical challenge for deploying neural speech codecs in real-world acoustic scenarios where background noise is often inevitable. A key observation we m…
Universal Discrete-Domain Speech Enhancement
Fei Liu, Yang Ai, Ye-Xin Lu +3
In real-world scenarios, speech signals are inevitably corrupted by various types of interference, making speech enhancement (SE) a critical task for robust speech processing. Howe…
Understanding Textual Capability Degradation in Speech LLMs via Parameter Importance Analysis
Chao Wang, Rui-Chen Zheng, Yang Ai +1
The integration of speech into Large Language Models (LLMs) has substantially expanded their capabilities, but often at the cost of weakening their core textual competence. This de…
Neural Speech Separation with Parallel Amplitude and Phase Spectrum Estimation
Fei Liu, Yang Ai, Zhen-Hua Ling
This paper proposes APSS, a novel neural speech separation model with parallel amplitude and phase spectrum estimation. Unlike most existing speech separation methods, the APSS dis…
A High-Quality and Low-Complexity Streamable Neural Speech Codec with Knowledge Distillation
En-Wei Zhang, Hui-Peng Du, Xiao-Hang Jiang +2
While many current neural speech codecs achieve impressive reconstructed speech quality, they often neglect latency and complexity considerations, limiting their practical deployme…
A Distilled Low-Latency Neural Vocoder with Explicit Amplitude and Phase Prediction
Hui-Peng Du, Yang Ai, Zhen-Hua Ling
The majority of mainstream neural vocoders primarily focus on speech quality and generation speed, while overlooking latency, which is a critical factor in real-time applications.…