In nonacoustic speech recognition based on electromyography, i.e. on electrical muscle activity captured by noninvasive surface electrodes, differences between recording sessions are known to cause deteriorating system accuracy. Efficient adaptation of an existing system to an unseen recording session is therefore imperative for practical usage scenarios. We report on a meta-learning approach to pretrain a deep neural network frontend for a myoelectric speech recognizer in a way that it can be easily adapted to a new session. Fine-tuning this specially pretrained network yields lower Word Error Rates and higher frame accuracies than fine-tuning a conventionally pretrained network, without creating an increased computational burden on a possibly mobile device.