Report
oboVox Far Field Speaker Recognition: A Novel Data Augmentation Approach with Pretrained Models
| العنوان: | oboVox Far Field Speaker Recognition: A Novel Data Augmentation Approach with Pretrained Models |
|---|---|
| المؤلفون: | Dip, Muhammad Sudipto Siam, Hasan, Md Anik, Bipro, Sapnil Sarker, Raiyan, Md Abdur, Motin, Mohammod Abdul |
| سنة النشر: | 2024 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Electrical Engineering and Systems Science - Audio and Speech Processing, Computer Science - Sound |
| الوصف: | In this study, we address the challenge of speaker recognition using a novel data augmentation technique of adding noise to enrollment files. This technique efficiently aligns the sources of test and enrollment files, enhancing comparability. Various pre-trained models were employed, with the resnet model achieving the highest DCF of 0.84 and an EER of 13.44. The augmentation technique notably improved these results to 0.75 DCF and 12.79 EER for the resnet model. Comparative analysis revealed the superiority of resnet over models such as ECPA, Mel-spectrogram, Payonnet, and Titanet large. Results, along with different augmentation schemes, contribute to the success of RoboVox far-field speaker recognition in this paper Comment: 5 pages, 2 figures |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2409.10240 |
| رقم الانضمام: | edsarx.2409.10240 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |