EMG-based speech detection and communication

A system comprising:

• a storage device of an electromyograph (EMG) communication device;
• and
• at least one processor of the EMG communication device configured to perform operations comprising:
• detecting a trigger word by a microphone of the EMG communication device;
• transitioning the EMG communication device between an overt calibration mode to perform overt calibration of a machine learning technique and a covert calibration mode;
• in response to detecting the trigger word, detecting, by one or more EMG electrodes of the EMG communication device, subthreshold muscle activation signals of one or more muscles associated with speech production, the subthreshold muscle activation signals being generated in response to inner speech of a user;
• applying the machine learning technique to the subthreshold muscle activation signals to estimate one or more speech features corresponding to the subthreshold muscle activation signals, the machine learning technique being trained to establish a relationship between a plurality of training subthreshold muscle activation signals and ground truth speech features;
• generating visual or audible output based on the one or more speech features;
• and
• causing the visual or audible output to be processed by a messaging application to engage a feature of the messaging application.

A non-transitory machine-readable storage medium comprising instructions that, when executed by one or more processors of a machine, cause the machine to perform operations comprising:

• detecting a trigger word by a microphone of an EMG communication device;
• transitioning the EMG communication device between an overt calibration mode to perform overt calibration of a machine learning technique and a covert calibration mode;
• in response to detecting the trigger word, detecting, by one or more EMG electrodes of the EMG communication device, subthreshold muscle activation signals of one or more muscles associated with speech production, the subthreshold muscle activation signals being generated in response to inner speech of a user;
• applying the machine learning technique to the subthreshold muscle activation signals to estimate one or more speech features corresponding to the subthreshold muscle activation signals, the machine learning technique being trained to establish a relationship between a plurality of training subthreshold muscle activation signals and ground truth speech features;
• generating visual or audible output based on the one or more speech features;
• and
• causing the visual or audible output to be processed by a messaging application to engage a feature of the messaging application.