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.
Instead of relying on acoustic signals, SSIs exploit non -vocal modalities that capture articulatory and physiological activity underlying speech production
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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.