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.

翻译：当前高性能的皮层内语音神经假体虽能实现低词错误率，但在推理阶段通常依赖外部语言模型，这会增加内存占用、计算开销和延迟。本研究旨在探讨无需此类模型时是否可实现有意义的字符级解码。我们提出一种基于Conformer的端到端神经解码器，直接对来自肌萎缩侧索硬化症（ALS）参与者的皮层内记录信号进行训练。在无任何外部语言模型的情况下，该系统在保留验证数据上实现了23.80%的字符错误率（CER）。分析表明，性能波动主要由跨会话信号衰减导致，而主要错误源于错误的词边界分割。这些结果证明，在完全端到端框架中实现有效的字符级解码是可行的，能为下游语言处理提供强大的神经信号。