Скачать или смотреть Improving Speaker Verification Model for Low-Resources Languages

Maksym Kizitskyi, Olena Turuta and Oleksii Turuta

Kharkiv National University of Radio Electronics, 14 Nauky Ave., Kharkiv, 61166, Ukraine

Speaker verification is an essential task in speech processing. Implementation this task based on convolutional neural networks. Several key metrics were evaluated, including equal error rate and precision top-K, and were compared the performance of different architectures and loss functions. The experiments are conducted using a Ukrainian dataset and include comparisons of models trained on multilingual data, as well as models trained on clean and augmented data. The results are presented in tables and figures, showing that even for low-resource languages, the models can achieve good performance metrics. The authors also discuss the implications of their findings and the potential for transferring skills to other languages. The paper provides valuable insights for researchers working in the field of speaker verification.