A cross-species pretrained neural encoder combined with end-to-end training and audio LLMs reduces word error rate in neural speech decoding from 24.69% to 10.22% while aligning attempted and imagined speech.
Towards an end-to-end framework for invasive brain signal decoding with large language models.arXiv preprint arXiv:2406.11568,
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A cross-species neural foundation model for end-to-end speech decoding
A cross-species pretrained neural encoder combined with end-to-end training and audio LLMs reduces word error rate in neural speech decoding from 24.69% to 10.22% while aligning attempted and imagined speech.