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Decoding spoken speech from neural activity in the brain is a fast-emergingresearch topic, as it could enable communication for people who havedifficulties with producing audible speech. For this task, electrocorticography(ECoG) is a common method for recording brain activity with high temporalresolution and high spatial precision. However, due to the risky surgicalprocedure required for obtaining ECoG recordings, relatively little of thisdata has been collected, and the amount is insufficient to train a neuralnetwork-based Brain-to-Speech (BTS) system. To address this problem, we proposeBrainTalker-a novel BTS framework that generates intelligible spoken speechfrom ECoG signals under extremely low-resource scenarios. We apply a transferlearning approach utilizing a pre-trained self supervised model, Wav2Vec 2.0.Specifically, we train an encoder module to map ECoG signals to latentembeddings that match Wav2Vec 2.0 representations of the corresponding spokenspeech. These embeddings are then transformed into mel-spectrograms usingstacked convolutional and transformer-based layers, which are fed into a neuralvocoder to synthesize speech waveform. Experimental results demonstrate ourproposed framework achieves outstanding performance in terms of subjective andobjective metrics, including a Pearson correlation coefficient of 0.9 betweengenerated and ground truth mel spectrograms. We share publicly available Demosand Code.

BrainTalker: Low-Resource Brain-to-Speech Synthesis with Transfer Learning using Wav2Vec 2.0