ArticleNature machine intelligence2024
A neural speech decoding framework leveraging deep learning and speech synthesis.
Article in Nature machine intelligence, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 1 of them a synthesis that pooled it.
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Who cites it
27 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial intelligence for brain-to-speech decoding in paralysis: a systematic review.BMC medical informatics and decision making · 2026Pooled it
- Real-time detection of spoken speech from unlabeled ECoG signals: a pilot study with an ALS participant.Journal of neural engineering · 2025Trial
- Noise-Tunable Memristor Enabling Programmable Probabilistic Neurons for Frequency-Selective Time-Series Signal Encoding.Advanced materials (Deerfield Beach, Fla.) · 2026Article
- Speech Brain-Computer Interfaces: A New Pathway for Restoring Communication in Anarthria.Neuroscience bulletin · 2026Article
- Neurotechnological Natives and the Neurotechnology Shift: A Research Agenda for the Brain-as-Interface Era.Annals of the New York Academy of Sciences · 2026Review
- From trade-offs to translation: An interdisciplinary roadmap for neurotechnology.Science advances · 2026Review
- A Fine-grained Spatiotemporal ECoG Dataset during Speech Perception in Tonal Language.Scientific data · 2026Article
- Full-Stack Architectures for Intelligent Brain-Computer Interfaces.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- 3M-CPSEED, An EEG-based Dataset for Chinese Pinyin Production in Overt, Mouthed, and Imagined Speech.Scientific data · 2025Article
- Real-time decoding of full-spectrum Chinese using brain-computer interface.Science advances · 2025Article
- Transfer learning via distributed brain recordings enables reliable speech decoding.Nature communications · 2025Article
- Intraoperative Techniques for Language Mapping in Brain Surgery: A Comparison Between Direct Electrical Stimulation (DES) and Electrocorticography (ECoG).Brain and behavior · 2025Review
- Progress, challenges and future of linguistic neural decoding with deep learning.Communications biology · 2025Review
- A Hierarchical Multi-Feature Point Cloud Lithology Identification Method Based on Feature-Preserved Compressive Sampling (FPCS).Sensors (Basel, Switzerland) · 2025Article
- Reading specific memories from human neurons before and after sleep.bioRxiv : the preprint server for biology · 2025Article
- Neuronal Decoding of Decisions in Multidimensional Feature Space Using a Gated Recurrent Variational Autoencoder.bioRxiv : the preprint server for biology · 2025Article
- Domain general noise reduction for time series signals with Noisereduce.Scientific reports · 2025Article
- An instantaneous voice-synthesis neuroprosthesis.Nature · 2025Article
- VocalMind: A Stereotactic EEG Dataset for Vocalized, Mimed, and Imagined Speech in Tonal Language.Scientific data · 2025Article
- Recent applications of EEG-based brain-computer-interface in the medical field.Military Medical Research · 2025Review
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10 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Decoding human speech from neural signals is essential for brain-computer interface (BCI) technologies that aim to restore speech in populations with neurological deficits. However, it remains a highly challenging task, compounded by the scarce availability of neural signals with corresponding speech, data complexity and high dimensionality. Here we present a novel deep learning-based neural speech decoding framework that includes an ECoG decoder that translates electrocorticographic (ECoG) signals from the cortex into interpretable speech parameters and a novel differentiable speech synthesizer that maps speech parameters to spectrograms. We have developed a companion speech-to-speech auto-encoder consisting of a speech encoder and the same speech synthesizer to generate reference speech parameters to facilitate the ECoG decoder training. This framework generates natural-sounding speech and is highly reproducible across a cohort of 48 participants. Our experimental results show that our models can decode speech with high correlation, even when limited to only causal operations, which is necessary for adoption by real-time neural prostheses. Finally, we successfully decode speech in participants with either left or right hemisphere coverage, which could lead to speech prostheses in patients with deficits resulting from left hemisphere damage.
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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.