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Digital Voicing of Silent Speech
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In this paper, we consider the task of digitally voicing silent speech, where silently mouthed words are converted to audible speech based on electromyography (EMG) sensor measurements that capture muscle impulses. While prior work has focused on training speech synthesis models from EMG collected during vocalized speech, we are the first to train from EMG collected during silently articulated speech. We introduce a method of training on silent EMG by transferring audio targets from vocalized to silent signals. Our method greatly improves intelligibility of audio generated from silent EMG compared to a baseline that only trains with vocalized data, decreasing transcription word error rate from 64% to 4% in one data condition and 88% to 68% in another. To spur further development on this task, we share our new dataset of silent and vocalized facial EMG measurements.
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Cited by 2 Pith papers
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Brain-to-Text Decoding with Context-Aware Neural Representations and Large Language Models
Diphone-based marginalization plus LLM refinement achieves state-of-the-art word error rate (5.77%) and phoneme error rate (15.34%) on the Brain-to-Text 2024 benchmark.
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From Silent Signals to Natural Language: A Dual-Stage Transformer-LLM Approach
A transformer ASR with GPT-2 post-correction reduces word error rate for EMG-based silent speech recognition from 36% to 30% on the Digital Voicing test set.
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