The adoption of voice user interface (VUI) will promote network automation with enhanced efficiency with reduced simplicity and operating expense in Industry 5.0. Given the noisy environments, speech denoising is indispensable for the VUI in Internet of Things (IoT) or Industrial IoT (IIoT). Despite Transformer's recent success in speech denoising, the adopted full self-attention suffers from quadratic complexity, which challenges the computational power of the IoT/IIoT components. Considering the strong local correlations of speech signals, a speech-oriented sparse attention denoising scheme is developed to keep the meaningful local and global dependencies while mitigating the redundant attentions, resulting in a significant reduction in computational complexity. With the full self-attention as the baseline, experimental results revealed that the proposed scheme achieves a better denoising performance and yields a lower computational cost, indicating the strong potential for various VUI application scenarios in IoT and IIoT toward Industry 5.0.

中文翻译：

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面向工业 5.0 的语音用户界面的面向语音的稀疏注意力去噪


语音用户界面 (VUI) 的采用将促进网络自动化，提高效率，同时降低工业 5.0 的简单性和运营费用。考虑到嘈杂的环境，语音降噪对于物联网 (IoT) 或工业物联网 (IIoT) 中的 VUI 来说是必不可少的。尽管 Transformer 最近在语音去噪方面取得了成功，但所采用的完全自注意力却存在二次复杂度，这对 IoT/IIoT 组件的计算能力提出了挑战。考虑到语音信号的强局部相关性，开发了一种面向语音的稀疏注意力去噪方案，以保持有意义的局部和全局依赖性，同时减轻冗余注意力，从而显着降低计算复杂度。以完全自注意力为基准，实验结果表明，所提出的方案实现了更好的去噪性能并产生了更低的计算成本，表明物联网和工业物联网中的各种 VUI 应用场景在迈向工业 5.0 方面具有强大的潜力。