Implementation of active data Selection algorithms for data choosing in ASV systems
In the field of voice spoof detection, training countermea- sures (CM) that effectively generalize to various unseen tests is a per- sistent challenge. While strategies such as data augmentation and self- supervised learning have been employed to enhance CM, their limited performance still requires...
- Autores:
-
Jiménez Garizao, Jesús Alberto
- Tipo de recurso:
- Trabajo de grado de pregrado
- Fecha de publicación:
- 2023
- Institución:
- Universidad de los Andes
- Repositorio:
- Séneca: repositorio Uniandes
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.uniandes.edu.co:1992/73644
- Acceso en línea:
- https://hdl.handle.net/1992/73644
- Palabra clave:
- Active learning
Voice anti-spoofing
Countermeasure
Logical access
Deep learning
Ingeniería
- Rights
- embargoedAccess
- License
- Attribution 4.0 International
| Summary: | In the field of voice spoof detection, training countermea- sures (CM) that effectively generalize to various unseen tests is a per- sistent challenge. While strategies such as data augmentation and self- supervised learning have been employed to enhance CM, their limited performance still requires additional approaches. This research focuses on the use of active learning (AL) to select and eliminate training data in CM training, addressing the need to optimize data selection and im- prove model effectiveness. The study proposes several active learning algorithms that offer substantial improvements in detection error rates across multiple datasets in automatic speaker verification (ASV) sys- tems, these are based on the ASVspoof 2019 and the HABLA sets. Thus, these contributions are expected to be valuable for future research and applications in this domain, significantly enhancing model effectiveness. |
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