SpecAugment Impact on Automatic Speaker Verification System
Muhammad Yusuf Faisal, Suyanto Suyanto · 2019 International Seminar on Research of Information Technology and Intelligent Systems (ISRITI) · 2019
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 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 DNNs produces an Equal Error Rate (EER) of 18.1% that is better than the GMM system with EER of 19.0%. And after combining with a traditional augmentation technique, the DNNs also gives a better EER of 15.3% than GMM with EER of 15.7%.