Abstract
Objective
Brain-to-voice neuroprostheses can synthesize speech from the neural activity of people who have lost the ability to speak, enabling more natural and expressive communication. Assessing the intelligibility of synthesized speech, however, remains challenging because there is no ground-truth acoustic signal. The field instead relies on human transcription, which is costly and difficult to scale or compare across studies. Automatic speech recognition (ASR) offers a potential alternative, but state-of-the-art ASR models are optimized for transcription accuracy on typical fluent speech and may not reflect how humans perceive atypical synthesized speech.
Approach
We collected human transcriptions for ∼14.5 hours of synthesized speech from intracortical brain-to-voice decoders for one participant (yielding ∼72 hours of transcribed speech). We used the human-transcribed synthesized speech to fine-tune Whisper-large-v3 with low-rank adaptation (LoRA) so that its word error rate (WER) approximates human WER. We then evaluated the model on held-out sentences.
Main results
Fine-tuning increased the Pearson correlation between model WER and human WER from 0.62 to 0.76. The fine-tuned model’s WER closely tracked human WER across evaluation sets, including speech synthesized by unseen brain-to-voice decoders. Close agreement with human WER was achieved with only 1.5 hours of synthesized speech.
Significance
These results demonstrate that human-calibrated ASR can provide a scalable measure of brain-to-voice intelligibility that reflects human transcription more closely than zero-shot ASR does.
Full Text
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