A Common Measure of Communication for Speech Brain-Computer Interfaces
Speech brain-computer interfaces (speech BCIs) translate neural activity into language, offering a path towards restoring speech for people with paralysis and, more broadly, enabling new forms of natural human-computer interaction. Despite this promise, the field lacks a common measure of progress because systems use different datasets, recording methods, types of speech, and vocabularies, so their reported scores are rarely comparable. Underlying this measurement problem are two unresolved questions: (i) what distribution of words should a speech BCI enable a user to communicate, and (ii) how
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- PossiblePossibly related (embedding) · 59%BCI combines adaptive AI with human learning for better performance. - Psychology Today →
- PossiblePossibly related (embedding) · 50%Towards general auditory intelligence for machine listening and speaking →
- PossiblePossibly related (embedding) · 46%Talk, talk, talk: The rise of AI dictation tools at work - Computerworld →
- FuzzySimilar title/name (fuzzy) · 87%huggingface/speech-to-speech →
“Fuzzy title match (0.94): “A Common Measure of Communication for Speech Brain-Computer ” ≈ “huggingface/speech-to-speech””
- LinkedLinked via arxiv author · 85%Dulhan Jayalath →
“A Common Measure of Communication for Speech Brain-Computer Interfaces”
- LinkedLinked via arxiv author · 85%Benjamin Ballyk →
“A Common Measure of Communication for Speech Brain-Computer Interfaces”
- LinkedLinked via arxiv author · 85%Oiwi Parker Jones →
“A Common Measure of Communication for Speech Brain-Computer Interfaces”