A Brief Overview of Unsupervised Neural Speech Representation Learning
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Accepted author manuscript, 963 KB, PDF document
Unsupervised representation learning for speech processing has matured greatly in the last few years. Work in computer vision and natural language processing has paved the way, but speech data offers unique challenges. As a result, methods from other domains rarely translate directly. We review the development of unsupervised representation learning for speech over the last decade. We identify two primary model categories: self-supervised methods and probabilistic latent variable models. We describe the models and develop a comprehensive taxonomy. Finally, we discuss and compare models from the two categories.
Original language | English |
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Title of host publication | Proceedings of 2nd Workshop on Self-supervised Learning for Audio and Speech Processing |
Number of pages | 13 |
Publisher | Association for the Advancement of Artificial Intelligence |
Publication date | 2022 |
Publication status | Published - 2022 |
Event | Workshop on Self-supervised Learning for Audio and Speech Processing - Duration: 28 Feb 2022 → … Conference number: 2 https://aaai-sas-2022.github.io/ |
Workshop
Workshop | Workshop on Self-supervised Learning for Audio and Speech Processing |
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Nummer | 2 |
Periode | 28/02/2022 → … |
Internetadresse |
ID: 338603013