# Brain-to-Text Decoding with Context-Aware Neural Representations and Large Language Models Source: [Brain-to-Text Decoding with Context-Aware Neural Representations and Large Language Models]() Collection index: [llms.txt]() Data updated: 2025-10-26T06:57:29.486Z Authors: Jingyuan Li, Trung Le, Chaofei Fan, Mingfei Chen, Eli Shlizerman Published: 2024-11-16T01:34:44.000Z ## Abstract Decoding attempted speech from neural activity offers a promising avenue for restoring communication abilities in individuals with speech impairments. Previous studies have focused on mapping neural activity to text using phonemes as the intermediate target. While successful, decoding neural activity directly to phonemes ignores the context dependent nature of the neural activity-to-phoneme mapping in the brain, leading to suboptimal decoding performance. In this work, we propose the use of diphone - an acoustic representation that captures the transitions between two phonemes - as the context-aware modeling target. We integrate diphones into existing phoneme decoding frameworks through a novel divide-and-conquer strategy in which we model the phoneme distribution by marginalizing over the diphone distribution. Our approach effectively leverages the enhanced context-aware representation of diphones while preserving the manageable class size of phonemes, a key factor in simplifying the subsequent phoneme-to-text conversion task. We demonstrate the effectiveness of our approach on the Brain-to-Text 2024 benchmark, where it achieves state-of-the-art Phoneme Error Rate (PER) of 15.34% compared to 16.62% PER of monophone-based decoding. When coupled with finetuned Large Language Models (LLMs), our method yields a Word Error Rate (WER) of 5.77%, significantly outperforming the 8.93% WER of the leading method in the benchmark. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Word Decoding on Willett 2023 (test)](): Word Decoding; Willett 2023 (test); WER: 5.77. - [Speech Decoding on Brain-to-Text 2024 (hold-out)](): Speech Decoding; Brain-to-Text 2024 (hold-out); WER: 5.77. - [Phoneme Decoding on Willett 2023 (test)](): Phoneme Decoding; Willett 2023 (test); PER: 15.34. ## Original sources and implementation [Original paper]() [PDF]()