Name
Abstract | ||
|---|---|---|
The human voice differs substantially between individuals. This facilitates automatic speaker verification (ASV) — recognizing a person from his/her voice. ASV accuracy has substantially increased throughout the past decade due to recent advances in machine learning, particularly deep learning methods. An unfortunate downside has been substantially increased complexity of ASV systems. To help non-experts to kick-start reproducible ASV development, a state-of-the-art toolkit implementing various ASV pipelines and functionalities is required. To this end, we introduce a new open-source toolkit, ASVtorch, implemented in Python using the widely used PyTorch machine learning framework. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1016/j.softx.2021.100697 | SoftwareX |
Keywords | DocType | Volume |
Speaker recognition,PyTorch,Deep learning | Journal | 14 |
ISSN | Citations | PageRank |
2352-7110 | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kong-Aik Lee | 1 | 709 | 60.64 |
| Ville Vestman | 2 | 29 | 6.42 |
| Tomi Kinnunen | 3 | 1323 | 86.67 |