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Self-Supervised Learning of Speech Representation via Redundancy Reduction

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2023

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Gesellschaft für Informatik e.V.

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Our proposed research aims to contribute to the field of SSL for speech processing by developing representations that effectively capture latent speaker statistics. A comprehensive evaluation in various downstream tasks will provide a thorough assessment of the representations’ suitability and performance. The outcomes of this research will advance our understanding and utilization of SSL in speech representation learning, ultimately enhancing speaker-related applications and their practical implications.

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Brima, Yusuf (2023): Self-Supervised Learning of Speech Representation via Redundancy Reduction. DC@KI2023: Proceedings of Doctoral Consortium at KI 2023. DOI: 10.18420/ki2023-dc-02. Gesellschaft für Informatik e.V.. pp. 11-19. Doctoral Consortium at KI 2023. Berlin. 45195

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