A GDPR-compliant Ecosystem for Speech Recognition with Transfer, Federated, and Evolutionary Learning

Di Jiang, Conghui Tan, Jinhua Peng, Chaotao Chen, Xueyang Wu, Weiwei Zhao, Yuanfeng Song, Yongxin Tong, Chang Liu, Qian Xu, Qiang Yang, Li Deng

Abstract: Automatic Speech Recognition (ASR) is playing a vital role in a wide range of real-world applications. However, Commercial ASR solutions are typically “one-size-fits-all” products and clients are inevitably faced with the risk of severe performance degradation in field test. Meanwhile, with new data regulations such as the European Union’s General Data Protection Regulation (GDPR) coming into force, ASR vendors, which traditionally utilize the speech training data in a centralized approach, are becoming increasingly helpless to solve this problem, since accessing clients’ speech data is prohibited. Here, we show that by seamlessly integrating three machine learning paradigms (i.e., Transfer learning, Federated learning, and Evolutionary learning (TFE)), we can successfully build a win-win ecosystem for ASR clients and vendors and solve all the aforementioned problems plaguing them. Through large-scale quantitative experiments, we show that with TFE, the clients can enjoy far better ASR solutions than the “one-size-fits-all” counterpart, and the vendors can exploit the abundance of clients’ data to effectively refine their own ASR products.