On-device automatic speech recognition systems face several challenges
compared to server-based systems. They have to meet stricter constraints in
terms of speed, disk size and memory while maintaining the same accuracy. Often
they have to serve several applications with different distributions at once,
such as communicating with a virtual assistant and speech-to-text. The simplest
solution to serve multiple applications is to build application-specific
(language) models, but this leads to an increase in memory. Therefore, we
explore different data- and architecture-driven language modeling approaches to
build a single application-agnostic model. We propose two novel feed-forward
architectures that find an optimal trade off between different on-device
constraints. In comparison to the application-specific solution, one of our
novel approaches reduces the disk size by half, while maintaining speed and
accuracy of the original model.

本研究旨在探索不同的数据和架构驱动的语言建模方法，以构建一个单一应用程序不可知的模型，其优化了设备内存使用，同时保持了速度和准确性。其中两种前馈体系结构可以在不同设备约束之间找到最佳权衡，与特定应用程序的解决方案相比，可将硬盘占用减半。