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Repair common Windows errors and clear accumulated junk for a smoother, more stable PC - no reinstall needed.Free scan · no reinstallSpeech recognition turns spoken audio into estimated words. Brain-to-text decoding turns recorded neural activity associated with intended or attempted speech into text. The methods can share tools such as machine learning and language models, but they start from different signals and are studied in different settings. Brain-to-text is not a routine way to read arbitrary thoughts.
What is the key difference?
The input signal is the defining distinction. Automatic speech recognition (ASR) receives speech as audio, from a microphone or recording, and estimates what was said. Brain-to-text systems record neural activity and use a decoder to estimate linguistic content.
| Dimension | Speech recognition (ASR) | Brain-to-text decoding |
|---|---|---|
| Input | Spoken audio | Neural recordings associated with a defined task, such as attempted speech |
| Recording method | Microphone or audio file | Methods in the cited work include implanted electrodes, electrocorticography (ECoG), magnetoencephalography (MEG), and electroencephalography (EEG) |
| What the system estimates | The words spoken in the audio | Linguistic units or words inferred from neural activity |
| Typical context in the cited evidence | Processing speech audio | Research demonstrations with specified participants, tasks, and recording setups |
NIST defines ASR as technology that accepts speech as input and determines what was spoken. NIST’s Automatic Speech Recognition glossary entry is dated June 12, 2023. ASR does not need brain measurements: its input is audio.
How does each technology turn its input into text?
Speech recognition starts with audio
An ASR system processes a speech signal and estimates a sequence of words. Its task is to recover words from audio, which may include the speaker’s accent, speaking rate, background noise, and other characteristics of the recording.
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Brain-to-text starts with neural activity
A brain-to-text pipeline records neural activity, extracts useful features, and decodes those features into linguistic units or words. Some systems estimate phones or phonemes—basic sound units—and use a language model to help produce a plausible word sequence. A review describes speech neuroprostheses as transforming neural activity during intended speech into communication outputs such as text, audible sound, or orofacial movement. The review of speech neuroprostheses discusses this broader approach.
The methods overlap, but the inputs do not
Brain-to-text is not simply ASR applied to brain waves. The 2015 Brain-To-Text study modeled individual phones from intracranial ECoG recordings and borrowed techniques from ASR. A 2023 speech neuroprosthesis decoded phoneme probabilities from neural activity and combined them with a language model. Those shared decoding components do not make the systems equivalent: one starts with audio, the other with neural recordings. The 2015 Brain-To-Text study and the 2023 speech neuroprosthesis study describe those examples.
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Can a computer read thoughts?
The evidence described here does not establish a system that can freely read arbitrary thoughts. Brain-decoding results depend on what participants were asked to do, how their neural activity was recorded, and what the model was trained to decode. Attempting to speak, imagining speech, and typing a memorized sentence are distinct tasks; success on one does not show that a system can extract any private thought at will.
A 2025 NIH summary reports research involving attempted and imagined speech in four participants, and describes work exploring safeguards against unintended inner-speech output. This makes control over when a system decodes—and whether it produces output—an important consideration. The NIH summary on decoding inner speech describes that work.
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What have brain-to-text studies demonstrated?
Published results illustrate why performance figures need their task and measurement attached. Word error rate (WER) measures word-level errors; character error rate (CER) measures character-level errors. Results using different tasks, participants, signal types, vocabularies, and metrics are not a direct ranking.
| Study and task | Reported result | How to interpret it |
|---|---|---|
| Brain-To-Text, 2015: intracranial ECoG during speaking | Best reported WER: 25% | An early result for that system, not a current benchmark for the whole field. |
| Speech neuroprosthesis, 2023: attempted speech by one participant with ALS using an intracortical system | 62 words per minute; WER of 9.1% with a 50-word vocabulary and 23.8% with a 125,000-word vocabulary | Study-specific results from one participant and setup. The error rate differed substantially with vocabulary size. |
| Noninvasive decoding, 2026: 35 healthy volunteers typed briefly memorized sentences while brain activity was recorded | Mean CER of 29% with MEG and 65% with EEG | This was decoding in a constrained typing-and-memory task, not unrestricted speech or arbitrary thought decoding. CER is not directly comparable to WER. |
The 2015 study is described in Frontiers in Neuroscience; the 2023 results appear in Nature; and the noninvasive 2026 findings appear in Nature Neuroscience. The 2023 result is not a general product guarantee, and the 2026 task does not establish practical assistive communication from unrestricted thought.
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Does brain-to-text require surgery?
No single answer applies to every approach. The cited 2023 speech neuroprosthesis used an intracortical system, and the 2015 Brain-To-Text work used intracranial ECoG. The 2026 study tested noninvasive MEG and EEG. These methods differ in how they record neural activity, but a noninvasive demonstration on memorized sentences does not by itself establish equivalent performance for assistive communication. In a separate 2021 NIH account, a speech neuroprosthesis translated brain signals into words displayed on a screen; the featured study involved one participant and a limited vocabulary. The NIH report on that device describes the demonstration.
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Which technology is relevant to a particular task?
- Turning a spoken recording into a transcript: ASR is designed for this input—audio of speech.
- Communicating when a person cannot produce usable speech: a speech neuroprosthesis is a research direction that may decode neural activity linked to intended or attempted speech. The cited demonstrations are tied to specific participants and systems, not a general-purpose consumer feature.
- Decoding noninvasive brain signals: MEG and EEG have been studied, but the cited 2026 demonstration used briefly memorized sentences typed by healthy volunteers. Its results should not be treated as evidence of unrestricted thought reading.
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