标题： 脑到文本基准测试 '24：经验总结 显示英文标题

标题： Brain-to-Text Benchmark '24: Lessons Learned

摘要： 语音脑机接口旨在仅从神经活动解码一个人试图说什么，恢复因瘫痪而失去清晰说话能力的人的沟通能力。 Brain-to-Text 基准 '24 及相关竞赛的创建是为了促进将神经活动转换为文本的解码算法的进步。 在此，我们总结了截至 2024 年 6 月 1 日结束的竞赛所学到的经验（前四名参赛者也在录制的网络研讨会上分享了他们的经验）。 通过集成方法实现了最大的准确率提升，其中使用微调的大语言模型合并多个独立解码器的输出（所有前三名参赛者都采用了这种方法）。 通过改进基线循环神经网络（RNN）模型的训练方式也获得了性能提升，包括优化学习率调度和使用双音素训练目标。 然而，改进模型架构本身更为困难，尝试使用深度状态空间模型或变压器尚未显示出优于 RNN 基线的优势。 该基准将持续开放，以支持进一步提高脑到文本算法的准确性。 显示英文摘要

摘要： Speech brain-computer interfaces aim to decipher what a person is trying to say from neural activity alone, restoring communication to people with paralysis who have lost the ability to speak intelligibly. The Brain-to-Text Benchmark '24 and associated competition was created to foster the advancement of decoding algorithms that convert neural activity to text. Here, we summarize the lessons learned from the competition ending on June 1, 2024 (the top 4 entrants also presented their experiences in a recorded webinar). The largest improvements in accuracy were achieved using an ensembling approach, where the output of multiple independent decoders was merged using a fine-tuned large language model (an approach used by all 3 top entrants). Performance gains were also found by improving how the baseline recurrent neural network (RNN) model was trained, including by optimizing learning rate scheduling and by using a diphone training objective. Improving upon the model architecture itself proved more difficult, however, with attempts to use deep state space models or transformers not yet appearing to offer a benefit over the RNN baseline. The benchmark will remain open indefinitely to support further work towards increasing the accuracy of brain-to-text algorithms.