Brain-to-Speech: Prosody Feature Engineering and Transformer-Based Reconstruction
This chapter presents a novel approach to brain-to-speech (BTS) synthesis from intracranial electroencephalography (iEEG) data, emphasizing prosody-aware feature engineering and advanced transformer-based models for high-fidelity speech reconstruction. Driven by the increasing interest in decoding speech directly from brain activity, this work integrates neuroscience, artificial intelligence, and signal processing to generate accurate and natural speech. We introduce a novel pipeline for extracting key prosodic features directly from complex brain iEEG signals, including intonation, pitch, and rhythm. To effectively utilize these crucial features for natural-sounding speech, we employ adv...
| Authors | Mohammed Salah Al-Radhi, Géza Németh, Andon Tchechmedjiev et al. |
|---|---|
| Primary category | eess.SP |
| Published | 7 April 2026 |
| Last updated at source | 7 April 2026 |
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