INNER SPEECH ITERATIVE LEARNING LOOP
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United States of America
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Issued Date -
Apr 10, 2025
app pub date -
Oct 10, 2023
filing date -
Oct 10, 2023
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Abstract
Methods and systems are disclosed for iteratively training a user and a ML model to produce accurate inner speech outputs. The methods and systems access a ML model and perform a first training iteration in which EMG data corresponding to inner speech is processed by the machine learning model to decode the EMG data into a set of predicted phonemes, phoneme sounds, words or phrases. The methods and systems present the set of predicted phonemes, phoneme sounds, words or phrases to the user and form a first set of training data comprising the set of predicted phonemes, phoneme sounds, words or phrases, the EMG data, and the set of specified phonemes, phoneme sounds, words or phrases as ground truth information. The methods and systems update parameters of the ML model based on the first set of training data prior to starting a second training iteration.
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