AI 中文总结
本文提出StreamWSR这一可流式轻量波形域语音超分辨率模型，通过因果架构实现零前瞻流式推理，在16 kHz语音超分辨率任务上，以9M参数、2G FLOPs达到优于或相当的语音质量与可懂度。
AI 中文摘要
本文提出了StreamWSR，一种用于语音超分辨率（SR）的可流式神经波形域模型。通过采用具有紧凑帧级波形表示的全因果架构，所提出的StreamWSR支持零前瞻流式推理，同时避免基于声码器的重建和显式相位预测。具体而言，StreamWSR通过步长因果卷积将输入波形下采样为紧凑的帧级表示；随后，采用轻量的因果长短时建模主干，在因果约束下捕获局部波形结构和长程历史依赖；最后，通过因果转置卷积将建模输出转换回波形域，并通过残差连接与输入波形结合，生成最终的高分辨率语音。在16 kHz语音SR上的实验结果表明，与代表性的波形域和频谱域基线相比，StreamWSR实现了具有竞争力或更优的语音质量和可懂度，同时保持零前瞻流式优势，仅需9M参数和2G FLOPs。
英文摘要
This paper proposes StreamWSR, a Streamable neural Waveform-domain model for speech Super-Resolution (SR). By adopting a fully causal architecture with compact frame-level waveform representation, the proposed StreamWSR supports zero-look-ahead streaming inference while avoiding vocoder-based reconstruction and explicit phase prediction. Specifically, StreamWSR downsamples the input waveform into a compact frame-level representation using strided causal convolutions. Then, a lightweight causal long-short-term modeling backbone is employed to capture both local waveform structures and long-range historical dependencies under causal constraints. Finally, the modeled output is converted back to the waveform domain through a causal transposed-convolution and combined with the input waveform via a residual connection to generate the final high-resolution speech. Experimental results on 16 kHz speech SR show that StreamWSR achieves competitive or superior speech quality and intelligibility compared with representative waveform- and spectrum-based baselines, while maintaining a zero-look-ahead streaming advantage with only 9M parameters and 2G FLOPs.
