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Brain-to-text: decoding spoken phrases from phone representations in the brain
Christian Herff1, Dominic Heger1, Adriana de Pesters2
1Cognitive Systems Lab, Institute for Anthropomatics and Robotics, Karlsruhe Institute of Technology Karlsruhe, Germany.
Frontiers in Neuroscience
|July 1, 2015
Summary
Researchers developed Brain-To-Text, a system decoding continuous speech from brain activity. This breakthrough in neural decoding could enable direct human-machine communication using imagined speech.
Area of Science:
- Neuroscience
- Artificial Intelligence
- Speech Processing
Background:
- Previous studies showed limited success in decoding isolated speech aspects from neural signals.
- Decoding continuously spoken language from brain activity remained a significant challenge.
Purpose of the Study:
- To demonstrate the feasibility of decoding continuous speech into text from brain activity.
- To develop a system for real-time speech decoding from neural recordings.
Main Methods:
- Implemented an intracranial electrocorticographic (ECoG) recording system.
- Developed the Brain-To-Text system modeling single phones and using automatic speech recognition (ASR) techniques.
Main Results:
- Achieved word error rates as low as 25% and phone error rates below 50% in decoding continuous speech.
- Identified cortical regions crucial for processing individual speech sounds (phones).
Conclusions:
- The Brain-To-Text system represents a significant advancement in decoding continuous speech from neural signals.
- This technology is a key step towards enabling human-machine communication via imagined speech.
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