发表机构
University of Sherbrooke（谢布鲁克大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究针对人工耳蜗用户在嘈杂环境中语音理解困难的问题，提出受Deep ACE架构启发的脉冲神经网络，可同时实现语音增强与CI编码，在保持性能的同时能耗降低超6倍。
AI 中文摘要
人工耳蜗（CI）为重度至极重度听力损失患者恢复听觉，但CI用户在嘈杂环境中常难以理解语音。深度神经网络（DNN）在增强CI用户语音方面展现出潜力，但其高能耗使其不适用于低功耗CI处理器。而脉冲神经网络（SNN）性能相当，能耗却显著更低。因此，我们提出一种受Deep ACE架构启发的新型SNN，可同时实现语音增强与CI编码。该模型在语音编码短时客观可懂度（VSTOI）和信噪比提升（SNRi）得分上与Deep ACE相比具有竞争力，同时能耗降低超过6倍。
英文摘要
Cochlear implants (CI) restore hearing for individuals with severe to profound hearing loss. However, CI users often struggle to understand speech in noisy environments. Deep neural networks (DNN) have shown promise in enhancing speech for CI users, yet their high energy demands make them non-ideal for low-power CI processors. Spiking neural networks (SNN), on the other hand, offer comparable performance with significantly lower energy consumption. Hence, we propose a novel SNN inspired by the Deep ACE architecture that simultaneously performs speech enhancement and CI coding. Our model achieves competitive vocoded short-time objective intelligibility (VSTOI) and signal-to-noise ratio improvement (SNRi) scores compared to Deep ACE, while achieving more than a sixfold reduction in energy consumption.
