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Brain-to-Text Decoding with Context-Aware Neural Representations and Large Language Models by Li, Jingyuan; Le, Trung; Fan, Chaofei; Chen, Mingfei; Shlizerman, Eli is a scholarly article available to read on EtoBox.

What is Brain-to-Text Decoding with Context-Aware Neural Representations and Large Language Models about?

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%

Author

Li, Jingyuan; Le, Trung; Fan, Chaofei; Chen, Mingfei; Shlizerman, Eli

Published

2024

Language

EN