Blackrock 介绍端到端脑到文本接口 BIT：词错误率降至 10.22%
Decoding Inner Speech with an End-to-End Brain-to-Text Neural Interface
Blackrock Neurotech 介绍了一项预印本研究提出的端到端脑到文本框架 BIT，用单一可微神经网络把神经活动直接翻译成句子，词错误率从先前端到端方法的 24.69% 降至 10.22%。
BIT 把此前端到端方法的词错误率从 24.69% 降到 10.22%，并让尝试言语与想象言语的表征相互对齐。
Authors: Yizi Zhang, Linyang He, Chaofei Fan, Tingkai Liu, Han Yu, Trung Le, Jingyuan Li, Scott Linderman, Lea Duncker, Francis R Willett, Nima Mesgarani, Liam Paninsk
Abstract: Speech brain–computer interfaces (BCIs) aim to restore communication for people with paralysis by translating neural activity into text. Most systems use cascaded frameworks that decode phonemes before assembling sentences with ann-gram language model (LM), preventing joint optimization of all stages simultaneously. Here, we introduce an end-to-end BraIn-to-Text (BIT) framework that translates neural activity into coherent sentences using a single differentiable neural network. Central to our approach is a cross-task, cross-species pretrained neural encoder, whose representations transfer to both attempted and imagined
speech. In a cascaded setting with an n-gram LM, the pretrained encoder establishes a new state-of-the-art (SOTA) on the Brain-to-Text ’24 and ’25 benchmarks. Integrated end-to-end with audio large language models (LLMs) and trained with contrastive learning for cross-modal alignment, BIT reduces the word error rate (WER) of the prior end-to-end method from 24.69% to 10.22%. Notably, we find
that small-scale audio-LLMs markedly improve end-to-end decoding. Beyond record-setting performance, BIT aligns attempted and imagined speech embeddings to enable cross-task generalization. Altogether, our approach advances the integration of large, diverse neural datasets, paving the way for an end-to-end decoding framework that supports seamless, differentiable optimization.
Read the pre-print here.
来源：Blackrock Neurotech · blackrockneurotech.com