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., T ransfer learning, F ederated learning, and E volutionary 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.

中文翻译：

---

具有迁移、联合和进化学习的语音识别符合 GDPR 的生态系统

自动语音识别 (ASR) 在广泛的现实世界应用中发挥着至关重要的作用。然而，商业 ASR 解决方案通常是“一刀切”的产品，客户在现场测试中不可避免地面临性能严重下降的风险。同时，随着欧盟通用数据保护条例 (GDPR) 等新数据法规的生效，传统上集中使用语音训练数据的 ASR 供应商越来越无助于解决这个问题，因为访问客户的禁止语音数据。在这里，我们通过无缝集成三种机器学习范式（即，吨迁移学习，F联合学习，以及乙进化学习（TFE）），我们可以成功地为 ASR 客户和供应商构建一个双赢的生态系统，并解决上述所有困扰他们的问题。通过大规模的定量实验，我们表明，使用 TFE，客户可以享受比“一刀切”更好的 ASR 解决方案，并且供应商可以利用客户的大量数据有效地提炼自己的ASR 产品。