Title: Federated Acoustic Model Optimization for Automatic Speech Recognition
Authors: Conghui Tan, Di Jiang, Huaxiao Mo, Jinhua Peng, Yongxin Tong, Weiwei Zhao, Chaotao Chen, Rongzhong Lian, Yuanfeng Song and Qian Xu
Abstract: Traditional Automatic Speech Recognition (ASR) systems are usually trained with speech records centralized on the ASR vendor's machines. However, with data regulations such as General Data Protection Regulation (GDPR) coming into force, sensitive data such as speech records are not allowed to be utilized in such a centralized approach anymore. In this demonstration, we propose and show the method of federated acoustic model optimization in order to solve this problem. This demonstration does not only vividly show the underlying working mechanisms of the proposed method but also provides an interface for the user to customize its hyperparameters. With this demonstration, the audience can experience the effect of federated learning in an interactive fashion and we wish this demonstration would inspire more research on GDPR-compliant ASR technologies.
