从神经活动进行端到端的脑皮层内语音解码
End-to-End Intracortical Speech Decoding from Neural Activity
AI总结：
提出基于Conformer的端到端神经解码器，无需外部语言模型即可从肌萎缩侧索硬化症（ALS）患者的脑皮层内记录中实现字符级解码，字符错误率（CER）为23.80%。
摘要：
当前高性能的脑皮层内语音神经假体实现了低词错误率，但通常在推理过程中依赖外部语言模型，增加了内存、计算和延迟。在这项工作中，我们研究了在没有此类模型的情况下是否可以实现有意义的字符级解码。我们提出了一种基于Conformer的端到端神经解码器，直接训练自一名肌萎缩侧索硬化症（ALS）参与者的脑皮层内记录。在没有任何外部语言模型的情况下，该系统在留出验证数据上实现了23.80%的字符错误率（CER）。分析表明，性能变异性由会话间信号退化驱动，而主要错误源于错误的词边界分割。这些结果表明，在完全端到端的框架中实现有效的字符级解码是可能的，为下游语言处理提供了强大的神经信号。
英文摘要：
Current high-performing intracortical speech neuroprostheses achieve low word error rates but typically rely on external language models during inference, increasing memory, computation, and latency. In this work, we investigate whether meaningful character-level decoding is achievable without such models. We propose an end-to-end Conformer-based neural decoder trained directly on intracortical recordings from a participant with amyotrophic lateral sclerosis (ALS). Without any external language model, the system achieves a character error rate (CER) of 23.80\% on held-out validation data. Analysis shows that performance variability is driven by inter-session signal degradation, while dominant errors arise from incorrect word boundary segmentation. These results demonstrate that effective character-level decoding is possible in a fully end-to-end framework, providing a strong neural signal for downstream linguistic processing.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
