ArticleJournal of speech, language, and hearing research : JSLHR2024
Neural Decoding of Spontaneous Overt and Intended Speech.
Article in Journal of speech, language, and hearing research : JSLHR, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.
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Who cites it
4 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
- Language dysfunction as a primary feature of cognitive decline in neurological populations.Journal of neural transmission (Vienna, Austria : 1996) · 2026Review
- Optimal sensor set for MEG-based spontaneous and intended speech decoding toward practical communication.Frontiers in human neuroscience · 2026Article
- Artificial Intelligence in Communication Sciences and Disorders: Introduction to the Forum.Journal of speech, language, and hearing research : JSLHR · 2024Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
purposeThe aim of this study was to decode intended and overt speech from neuromagnetic signals while the participants performed spontaneous overt speech tasks without cues or prompts (stimuli).
methodMagnetoencephalography (MEG), a noninvasive neuroimaging technique, was used to collect neural signals from seven healthy adult English speakers performing spontaneous, overt speech tasks. The participants randomly spoke the words yes or no at a self-paced rate without cues. Two machine learning models, namely, linear discriminant analysis (LDA) and one-dimensional convolutional neural network (1D CNN), were employed to classify the two words from the recorded MEG signals.
resultsLDA and 1D CNN achieved average decoding accuracies of 79.02% and 90.40%, respectively, in decoding overt speech, significantly surpassing the chance level (50%). The accuracy for decoding intended speech was 67.19% using 1D CNN.
conclusionsThis study showcases the possibility of decoding spontaneous overt and intended speech directly from neural signals in the absence of perceptual interference. We believe that these findings make a steady step toward the future spontaneous speech-based brain-computer interface.
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Registered trials
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