Optimized European Portuguese Speech-To-Text using Deep Learning
| dc.contributor.author | Medeiros, Eduardo | |
| dc.contributor.author | Corado, Leonel | |
| dc.contributor.author | Rato, Luis | |
| dc.contributor.author | Quaresma, Paulo | |
| dc.contributor.author | Salgueiro, Pedro | |
| dc.date.accessioned | 2023-02-15T15:56:42Z | |
| dc.date.available | 2023-02-15T15:56:42Z | |
| dc.date.issued | 2022-10 | |
| dc.description.abstract | We have developed an ASR system for European Portuguese implement ing the QuartzNet [3] architecture with the NeMo [4] framework. Two approaches were used in this work: from scratch and using transfer learning. The experiments were data-driven focused instead of algorithm finetuning. Experiments confirm that models developed using transfer learning have shown better results (WER=0.0513) than developing models from scratch (WER=0.1945). | por |
| dc.identifier.authoremail | nd | |
| dc.identifier.authoremail | nd | |
| dc.identifier.authoremail | lmr@uevora.pt | |
| dc.identifier.authoremail | pq@uevora.pt | |
| dc.identifier.authoremail | pds@uevora.pt | |
| dc.identifier.citation | Medeiros, E., Corado,L., Rato, L., Quaresma, P., Salgueiro, P., Optimized European Portuguese Speech-To-Text using Deep Learning, RECPAD2022, 28th Portuguese Conference on Pattern Recognition, School of Technology and Management – Politécnico de Leiria, 2022. | por |
| dc.identifier.scientificarea | 283 | por |
| dc.identifier.uri | http://hdl.handle.net/10174/34466 | |
| dc.language.iso | por | por |
| dc.peerreviewed | yes | por |
| dc.publisher | APRP | por |
| dc.rights | openAccess | por |
| dc.subject | speech | por |
| dc.subject | text | por |
| dc.subject | transfer | por |
| dc.subject | deep learning | por |
| dc.subject | portuguese | por |
| dc.title | Optimized European Portuguese Speech-To-Text using Deep Learning | por |
| dc.type | article | por |