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

Published: 05 May 2021 Publication History

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.

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    cover image ACM Transactions on Intelligent Systems and Technology
    ACM Transactions on Intelligent Systems and Technology  Volume 12, Issue 3
    June 2021
    218 pages
    ISSN:2157-6904
    EISSN:2157-6912
    DOI:10.1145/3460499
    Issue’s Table of Contents
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    Publication History

    Published: 05 May 2021
    Accepted: 01 January 2021
    Revised: 01 November 2020
    Received: 01 March 2020
    Published in TIST Volume 12, Issue 3

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    Author Tags

    1. Speech recognition
    2. federated learning
    3. transfer learning
    4. evolutionary learning

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