We introduce a framework for adapting a virtual keyboard to individual user behavior by modifying a Gaussian spatial model to use personalized key center offset means and, optionally, learned covariances. Through numerous real-world studies, we determine the importance of training data quantity and weights, as well as the number of clusters into which to group keys to avoid overfitting. While past research has shown potential of this technique using artificially-simple virtual keyboards and games or fixed typing prompts, we demonstrate effectiveness using the highly-tuned Gboard app with a representative set of users and their real typing behaviors. Across a variety of top languages, we achieve small-but-significant improvements in both typing speed and decoder accuracy.
翻译:我们引入了一个使虚拟键盘适应个人用户行为的框架,通过修改高斯空间模型来使用个性化键中心抵消手段和可选的学习共变。我们通过许多现实世界研究,决定了培训数据数量和重量的重要性,以及将键组成组以避免过度匹配的组群数量。虽然过去的研究表明了使用人工简单虚拟键盘和游戏或固定打字提示的这种技术的潜力,但我们使用高调的G板应用程序展示了有效性,该应用程序有一组有代表性的用户及其真实打字行为。在各种顶级语言中,我们在打字速度和解码精度两方面都取得了小但有意义的改进。