ArticleNature2019
Speech synthesis from neural decoding of spoken sentences.
Article in Nature, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 273 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
273 citing papers in PubMed, 1 synthesis or guideline pooled it, 870 citations in OpenAlex.
- Gray matter asymmetry in asymptomatic carotid stenosis.Human brain mapping · 2021Pooled it
- Real-time detection of spoken speech from unlabeled ECoG signals: a pilot study with an ALS participant.Journal of neural engineering · 2025Trial
- Generalizable neural decoding for clinically scalable brain-computer interfaces.npj biomedical innovations · 2026Review
- Real-time brain-controlled selective hearing enhances speech perception in multi-talker environments.Nature neuroscience · 2026Article
- In conversation with Edward Chang.Nature neuroscience · 2026Article
- Noninvasive decoding of typed sentences from human brain activity.Nature neuroscience · 2026Article
- A neural architecture for imagined and overt speech motor dynamics.Nature neuroscience · 2026Article
- Towards neuromorphic neurotechnologies: integrating brain-inspired computing with brain-computer interfaces.npj biomedical innovations · 2026Review
- Distinct neural processes link speech planning and execution.Nature human behaviour · 2026Article
- Cross-region neural signal reconstruction to lift electrode placement constraints in SSVEP brain-computer interfaces.npj biomedical innovations · 2026Article
- Frequency-dependent effects of motor thalamus deep brain stimulation on speech and swallowing.Nature communications · 2026Article
- Sensory-guided human-machine joint learning accelerates the acquisition of motor imagery brain computer interface control.Nature communications · 2026Article
- Perspectives in conducting task-based research in pediatric surgical epilepsy patients.bioRxiv : the preprint server for biology · 2026Article
- A Fine-grained Spatiotemporal ECoG Dataset during Speech Perception in Tonal Language.Scientific data · 2026Article
- Minimally invasive implantation of scalable high-density cortical microelectrode arrays for multimodal neural decoding and stimulation.Nature biomedical engineering · 2026Article
- Advances in neuroprostheses: interfaces, materials, and applications.Nano convergence · 2026Review
- Active inference and speech motor control.Psychonomic bulletin & review · 2026Review
- Distinct laminar origins of sensory-evoked high-gamma and low-frequency ECoG signals revealed by optogenetics.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- A Wireless, Battery-Free Artificial Throat Patch with Deep Learning for Emotional Speech Recognition.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Time-frequency-spatial channel attention network for semantic decoding: an exploratory EEG study.Medical & biological engineering & computing · 2026Article
213 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
Abstract
Technology that translates neural activity into speech would be transformative for people who are unable to communicate as a result of neurological impairments. Decoding speech from neural activity is challenging because speaking requires very precise and rapid multi-dimensional control of vocal tract articulators. Here we designed a neural decoder that explicitly leverages kinematic and sound representations encoded in human cortical activity to synthesize audible speech. Recurrent neural networks first decoded directly recorded cortical activity into representations of articulatory movement, and then transformed these representations into speech acoustics. In closed vocabulary tests, listeners could readily identify and transcribe speech synthesized from cortical activity. Intermediate articulatory dynamics enhanced performance even with limited data. Decoded articulatory representations were highly conserved across speakers, enabling a component of the decoder to be transferrable across participants. Furthermore, the decoder could synthesize speech when a participant silently mimed sentences. These findings advance the clinical viability of using speech neuroprosthetic technology to restore spoken communication.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.