Robust Speech Recognition Model Using Multi-Source Federal Learning after Distillation and Deep Edge Intelligence

Abstract

In order to further explore and solve the problem of distributed training edge intelligent application in edge devices close to the data side, a joint training method of multi-source federated learning is proposed for the robust speech recognition model. The joint training is performed directly on the edge computing device close to the data supply side. Under the open source Chinese speech recognition data set, the recognition accuracy is 95%. Compared with federated learning, there is only 2% performance reduction, but the communication load is reduced by 90%.