arXiv (All)
open access
Brain2Speech-Net: Intelligible, Real-Time Brain-to-Speech Synthesis Without Text Decoding
The loss of speech limits communication for individuals with paralysis. Restoring speech by synthesizing it directly from neural activity is challenging: intracortical data are scarce and lack aligned targets, so most systems rely on cascaded neural-to-text-to-speech pipelines that add latency and p
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Table 1_Optimal sensor set for MEG-based spontaneous and intended speech decoding toward practical communication.xlsx#1052875
- SENSE: Semantic Neural Speech Synthesis from Brain Dynamics via Spatial Graph Encoding#1141916
- Corrective Forcing: Unified Post-Training for Diffusions and Flows in Generative Speech Enhancement#1051058
- Corrective Forcing: Unified Post-Training for Diffusions and Flows in Generative Speech Enhancement#1028705
- Physiological Noise Augmentation Improves Non-Invasive Brain-to-Speech#343495
Record · ID 927743
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive