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Berkeley developed a streaming “brain-to-voice” neuroprosthesis which restores naturalistic, fluent, intelligible speech to a person who has paralysis.
Researchers adopted streaming transducer techniques similar to methods used by popular ASR methods like Siri or Alexa, and repurposed them for personalized brain-to-voice synthesis.
This approach resulted in significant improvements in the decoding speed of the brain-to-voice neuroprosthesis compared to prior approaches with longer delays.
Researchers also show continuous long-form brain-to-voice synthesis, robustness to model-generated auditory feedback, and out-of-vocabulary brain-to-voice synthesis.
Researchers adopted streaming transducer techniques similar to methods used by popular ASR methods like Siri or Alexa, and repurposed them for personalized brain-to-voice synthesis.
This approach resulted in significant improvements in the decoding speed of the brain-to-voice neuroprosthesis compared to prior approaches with longer delays.
Researchers also show continuous long-form brain-to-voice synthesis, robustness to model-generated auditory feedback, and out-of-vocabulary brain-to-voice synthesis.
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Berkeley developed a streaming “brain-to-voice” neuroprosthesis which restores naturalistic, fluent, intelligible speech to a person who has paralysis.
Researchers adopted streaming transducer techniques similar to methods used by popular ASR methods like Siri or Alexa, and repurposed them for personalized brain-to-voice synthesis.
This approach resulted in significant improvements in the decoding speed of the brain-to-voice neuroprosthesis compared to prior approaches with longer delays.
Researchers also show continuous long-form brain-to-voice synthesis, robustness to model-generated auditory feedback, and out-of-vocabulary brain-to-voice synthesis.
Researchers adopted streaming transducer techniques similar to methods used by popular ASR methods like Siri or Alexa, and repurposed them for personalized brain-to-voice synthesis.
This approach resulted in significant improvements in the decoding speed of the brain-to-voice neuroprosthesis compared to prior approaches with longer delays.
Researchers also show continuous long-form brain-to-voice synthesis, robustness to model-generated auditory feedback, and out-of-vocabulary brain-to-voice synthesis.
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