End-to-end Speech Translation with Self-supervised Speech Representations
For years speech translation has been faced as concatenation of speech recognition and machine translation. The powerful architectures of deep learning has made end-to-end speech translation feasible. The student will have to use the encoder-decoder architecture based on Transformer to build multili...
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| Tipo de recurso: | tesis de maestría |
| Fecha de publicación: | 2020 |
| País: | España |
| Institución: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2117/332513 |
| Acceso en línea: | https://hdl.handle.net/2117/332513 |
| Access Level: | acceso abierto |
| Palabra clave: | translation speech text end-to-end transformer self-supervision pase apc wav2vec |
| Sumario: | For years speech translation has been faced as concatenation of speech recognition and machine translation. The powerful architectures of deep learning has made end-to-end speech translation feasible. The student will have to use the encoder-decoder architecture based on Transformer to build multilingual speech translation systems. |
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