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

Application-Agnostic Language Modeling for On-Device ASR