SpecAugment Impact on Automatic Speaker Verification System
MUHAMMAD YUSUF FAISAL
Informasi Umum
Kode
20.04.1036
Klasifikasi
006.3 - Special Computer Methods- Artificial intelligence
Jenis
Karya Ilmiah - Skripsi (S1) - Reference
Subjek
Artificial Intelligence
Dilihat
223 kali
Informasi Lainnya
Abstraksi
An automatic speaker verification (ASV) is one of the challenging problem in speech processing since there are so many models of machine learnings those capable of synthesizing a fake speech from a given text. This paper discusses the impact of SpecAugment to state of the art methods such as Gaussian Mixture Models (GMM) and Deep Neural Networks (DNNs). Some experiments on a speech dataset sampled from the ASVSpoof2019, which is specially made to tackle the threat of spoofing, show that GMM produces an Equal Error Rate (EER) of 19.0% that is better than the DNNs system with EER of 24.0%. However, after combining with a traditional augmentation technique, the DNN gives a better EER of 15.3% than GMM with EER of 15.7%.
- MUG1E3 - ALJABAR LINEAR
- MUG2D3 - PROBABILITAS DAN STATISTIKA
Koleksi & Sirkulasi
Seluruh 1 koleksi sedang dipinjam
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Pengarang
| Nama | MUHAMMAD YUSUF FAISAL |
| Jenis | Perorangan |
| Penyunting | SUYANTO, NIKEN DWI WAHYU CAHYANI |
| Penerjemah |
Penerbit
| Nama | Universitas Telkom, S1 Informatika |
| Kota | Bandung |
| Tahun | 2020 |
Sirkulasi
| Harga sewa | IDR 0,00 |
| Denda harian | IDR 0,00 |
| Jenis | Non-Sirkulasi |
