Impact of sampling frequency on Equal Error Rate performance in speaker verification using Convolution Neural Network

Speaker verification is a crucial speech processing task with several applications in virtual assistants, security systems, and criminal investigations. However, standard techniques frequently struggle to strike the ideal balance between speed and precision, particularly in various acoustic environments. In this study, we provide a novel Speaker Verification Using Frequency Component Analysis method that exploits the influence of sampling frequency on the model’s performance. Our method captures minor speaker-specific properties hidden in audio data by combining feedforward neural networks with frequency-domain analysis. In the TIMIT data set, the model produces a state-of-the-art accuracy of X%. Existing techniques such as CNNs, Sincnet, and conventional MFCC-based classifiers are greatly outperformed by the suggested approach. The experimental results provide light on the relationship between sample rates and recognition performance. This realization has important ramifications for streamlining audio preparation pipelines. This work improves speaker verification duties and demonstrates the power of frequency-aware design, leading to better and more effective systems.