ReviewFrontiers in neuroscience2016
Automatic Speech Recognition from Neural Signals: A Focused Review.
Review in Frontiers in neuroscience, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers.
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Who cites it
39 citing papers in PubMed.
- Real-time detection of spoken speech from unlabeled ECoG signals: a pilot study with an ALS participant.Journal of neural engineering · 2025Trial
- A Fine-grained Spatiotemporal ECoG Dataset during Speech Perception in Tonal Language.Scientific data · 2026Article
- Optimal sensor set for MEG-based spontaneous and intended speech decoding toward practical communication.Frontiers in human neuroscience · 2026Article
- 3M-CPSEED, An EEG-based Dataset for Chinese Pinyin Production in Overt, Mouthed, and Imagined Speech.Scientific data · 2025Article
- An EEG-based Imagined Speech Database for comparing Paradigm Designs.Scientific data · 2025Article
- Brain activation during vocal motor imagery: a pilot functional near-infrared spectroscopy (fNIRS) study.Experimental brain research · 2025Article
- VocalMind: A Stereotactic EEG Dataset for Vocalized, Mimed, and Imagined Speech in Tonal Language.Scientific data · 2025Article
- Using data from cue presentations results in grossly overestimating semantic BCI performance.Scientific reports · 2024Article
- Neural Decoding of Spontaneous Overt and Intended Speech.Journal of speech, language, and hearing research : JSLHR · 2024Article
- Real-time detection of spoken speech from unlabeled ECoG signals: A pilot study with an ALS participant.medRxiv : the preprint server for health sciences · 2024Article
- Flexible Self-Powered Low-Decibel Voice Recognition Mask.Sensors (Basel, Switzerland) · 2024Article
- Online speech synthesis using a chronically implanted brain-computer interface in an individual with ALS.Scientific reports · 2024Article
- A neural speech decoding framework leveraging deep learning and speech synthesis.Nature machine intelligence · 2024Article
- Direct speech reconstruction from sensorimotor brain activity with optimized deep learning models.Journal of neural engineering · 2023Article
- Machine-Learning Methods for Speech and Handwriting Detection Using Neural Signals: A Review.Sensors (Basel, Switzerland) · 2023Review
- Intelligent speech technologies for transcription, disease diagnosis, and medical equipment interactive control in smart hospitals: A review.Computers in biology and medicine · 2023Review
- Linguistic representation of vowels in speech imagery EEG.Frontiers in human neuroscience · 2023Article
- Closed-Loop Adaptive Deep Brain Stimulation in Parkinson's Disease: Procedures to Achieve It and Future Perspectives.Journal of Parkinson's disease · 2023Review
- The Role of Artificial Intelligence in Decoding Speech from EEG Signals: A Scoping Review.Sensors (Basel, Switzerland) · 2022Article
- Dataset of Speech Production in intracranial.Electroencephalography.Scientific data · 2022Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Speech interfaces have become widely accepted and are nowadays integrated in various real-life applications and devices. They have become a part of our daily life. However, speech interfaces presume the ability to produce intelligible speech, which might be impossible due to either loud environments, bothering bystanders or incapabilities to produce speech (i.e., patients suffering from locked-in syndrome). For these reasons it would be highly desirable to not speak but to simply envision oneself to say words or sentences. Interfaces based on imagined speech would enable fast and natural communication without the need for audible speech and would give a voice to otherwise mute people. This focused review analyzes the potential of different brain imaging techniques to recognize speech from neural signals by applying Automatic Speech Recognition technology. We argue that modalities based on metabolic processes, such as functional Near Infrared Spectroscopy and functional Magnetic Resonance Imaging, are less suited for Automatic Speech Recognition from neural signals due to low temporal resolution but are very useful for the investigation of the underlying neural mechanisms involved in speech processes. In contrast, electrophysiologic activity is fast enough to capture speech processes and is therefor better suited for ASR. Our experimental results indicate the potential of these signals for speech recognition from neural data with a focus on invasively measured brain activity (electrocorticography). As a first example of Automatic Speech Recognition techniques used from neural signals, we discuss the
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