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Efficient, end-to-end and self-supervised methods for speech processing and generation

Author
Pascual, S.
Type of activity
Theses
Other related units
Department of Signal Theory and Communications
Defense's date
2020-01-31
URL
http://hdl.handle.net/2117/180792 Open in new window
Abstract
Deep learning has affected the speech processing and generation fields in many directions. First, end-to-end architectures allow the direct injection and synthesis of waveform samples. Secondly, the exploration of efficient solutions allow to implement these systems in computationally restricted environments, like smartphones. Finally, the latest trends exploit audio-visual data with least supervision. In this thesis these three directions are explored. Firstly, we propose the use of recent pseu...
Group of research
IDEAI-UPC - Intelligent Data Science and Artificial Intelligence Research Center
TALP - Centre for Language and Speech Technologies and Applications
VEU - Speech Processing Group
Citation
Pascual de la Puente, S. Efficient, end-to-end and self-supervised methods for speech processing and generation. Tesi doctoral, UPC, Departament de Teoria del Senyal i Comunicacions, 2020.

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