Speaker verification with ECAPA-TDNN trained on new dataset combined with Voxceleb and Korean

Speaker verification is becoming popular as a method of non-face-to-face identity authentication.It involves determining whether two voice data belong to the same speaker.In cases where the criminal's voice remains at the crime scene, it is vital to establish a speaker verification system that can accurately compare the two voice evidence.In this study, to achieve this, a new speaker verification system was built using a deep learning model for Korean language.High-dimensional voice data with a high variability like background noise made it necessary to use deep learning-based methods for speaker matching.To construct the matching algorithm, the ECAPA-TDNN model, known as the most famous deep learning system for speaker verification, was selected.A large dataset of the voice data, Voxceleb, collected from people of various nationalities without Korean.To study the appropriate form of datasets necessary for learning the Korean language, experiments were carried out to find out how Korean voice data affects the matching performance.The results showed that when comparing models learned only with Voxceleb and models learned with datasets combining Voxceleb and Korean datasets to maximize language and speaker diversity, the performance of learning data, including Korean, is improved for all test sets.