Loss of speech after paralysis is devastating, but circumventing motor-pathway injury by directly decoding speech from intact cortical activity has the potential to restore natural communication and self-expression. Recent discoveries have defined how key features of speech production are facilitated by the coordinated activity of vocal-tract articulatory and motor-planning cortical representations. In this Review, we highlight such progress and how it has led to successful speech decoding, first in individuals implanted with intracranial electrodes for clinical epilepsy monitoring and subsequently in individuals with paralysis as part of early feasibility clinical trials to restore speech. We discuss high-spatiotemporal-resolution neural interfaces and the adaptation of state-of-the-art speech computational algorithms that have driven rapid and substantial progress in decoding neural activity into text, audible speech, and facial movements. Although restoring natural speech is a long-term goal, speech neuroprostheses already have performance levels that surpass communication rates offered by current assistive-communication technology. Given this accelerated rate of progress in the field, we propose key evaluation metrics for speed and accuracy, among others, to help standardize across studies. We finish by highlighting several directions to more fully explore the multidimensional feature space of speech and language, which will continue to accelerate progress towards a clinically viable speech neuroprosthesis.

瘫痪后失去言语是毁灭性的，但通过直接解码完整皮层活动的言语来避免运动通路损伤，有可能恢复自然交流和自我表达。最近的发现定义了语音产生的关键特征是如何通过声道发音和运动规划皮层表征的协调活动来促进的。在这篇综述中，我们重点介绍了这些进展以及它如何成功实现语音解码，首先是在植入颅内电极的个体中进行临床癫痫监测，然后是在瘫痪个体中作为恢复言语的早期可行性临床试验的一部分。我们讨论高时空分辨率神经接口和最先进的语音计算算法的适应，这些算法推动了将神经活动解码为文本、可听语音和面部运动方面的快速和实质性进展。尽管恢复自然语音是一个长期目标，但语音神经假体的性能水平已经超过了当前辅助通信技术提供的通信速率。鉴于该领域的进展速度加快，我们提出了速度和准确性等关键评估指标，以帮助标准化研究。最后，我们强调了更全面地探索语音和语言的多维特征空间的几个方向，这将继续加速临床上可行的语音神经假体的进展。