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We introduce a framework for adapting a virtual keyboard to individual userbehavior by modifying a Gaussian spatial model to use personalized key centeroffset means and, optionally, learned covariances. Through numerous real-worldstudies, we determine the importance of training data quantity and weights, aswell as the number of clusters into which to group keys to avoid overfitting.While past research has shown potential of this technique usingartificially-simple virtual keyboards and games or fixed typing prompts, wedemonstrate effectiveness using the highly-tuned Gboard app with arepresentative set of users and their real typing behaviors. Across a varietyof top languages, we achieve small-but-significant improvements in both typingspeed and decoder accuracy.

Spatial model personalization in Gboard