BrainWhisperer: Leveraging Whisper for Speech Decoding in Neuroprosthetics

Decoding continuous speech from intracortical recordings holds transformative potential for individuals with severe motor impairments, yet current systems face fundamental barriers to real-world deployment: training data is scarce, neural signals are non-stationary across sessions, and the external language models required by state-of-the-art cascaded decoders impose memory requirements that preclude local, privacy-preserving inference. We introduce BrainWhisperer, a neural speech decoder that adapts Whisper–pretrained on approximately 680,000 hours of speech–to microelectrode array recordings via a convolutional front-end, hierarchical low-rank projections for non-stationarity, windowed self-attention in phoneme-selective encoder layers, and a multi-task objective combining CTC and cross-entropy losses. Subject-specific embedders enable cross-participant training within a unified architecture. Evaluated on publicly available Utah array datasets from BrainGate participants, BrainWhisperer achieves a word error rate of 8.5\% in end-to-end decoding on the Card benchmark--to the best of our knowledge, the best result reported in this setting. Cross-dataset training improves performance without participant-specific fine-tuning, pointing toward scalable foundation models for speech brain-computer interfaces.