Work-in-progress: Computation offloading of acoustic model for client-edge-based speech-recognition

Abstract

Speech recognition technology combined with artificial intelligence represents a quantum leap more accurate than past pattern recognition methods. And server-based system support for scalability, virtualization and huge amounts of unlimited storage resources that greatly contributed to the improvement of the accuracy of its prediction. However, the implementation of server-oriented reforms led to enormous latency and connectivity problems. Therefore, we propose a novel client-edge speech recognition system to enhance latency by using what we call semi-offloading technology. This proposal is promising big performance gains by offloading computing power-dependent tasks to edge nodes and processing throughput-dependent tasks by a client. The merit of semi-offloading as well as a division of workload allows for parallelism and re-ordering among the process. The experimental results show that, 23%∼62% improvement in response time.