title: Application-Agnostic Language Modeling for On-Device ASR
summary: 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.
paper_authors._id: 64658351611ae99d14d57623
paper_authors.name: Markus Nußbaum-Thom
paper_authors._id: 64658351611ae99d14d57624
paper_authors.name: Lyan Verwimp
paper_authors._id: 64658351611ae99d14d57625
paper_authors.name: Youssef Oualil
paper_title: Application-Agnostic Language Modeling for On-Device ASR
paper_summary: 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.
paper_ai_keywords: feed-forward architectures on-device automatic speech recognition application-specific (language) models application-agnostic model disk size memory constraints virtual assistant speech-to-text data-driven language modeling architecture-driven language modeling