Text-dependent speaker verification using discrete wavelet transform based on linear prediction coding
Sina Ketabi, Saeid Rashidi, Ali Fallah · Biomedical Signal Processing and Control · 2023
Presenting a system for verifying the identity of people so that the data could be recorded in the simplest possible way, performing identity verification with acceptable accuracy, and being robust to attacks and noises is one of some important issues in today's era. This paper presents a text-dependent speaker verification system in which features are extracted from the data using a method called discrete wavelet transform based on linear prediction coding. In addition to the conventional classification in these systems, a new method for classification in which one binary classification will perform for all speakers of the dataset can also be done, which will be introduced in this research. Then the performance of the designed system on ASVspoof 2015, ASVspoof 2017, ASVspoof 2019, AudioMNIST, and TIMIT datasets will be evaluated using accuracy, precision, sensitivity, specificity, F1-score, and equal error rate (EER) metrics. It will be shown that the EER for the case where classification is done once for each speaker is 0.14 ± 0.83 % for the ASVspoof 2017 dataset and the accuracy and EER for the case where classification is done once for all speakers is 100 ± 0.00 % and 0.00 ± 0.00 % for all datasets.