In recent years, speaker recognition has emerged as a pivotal research domain, particularly with the advent of Deep Neural Network (DNN)-based embeddings exhibiting remarkable results, even for short-duration audio and text-independent tasks. Speaker recognition is instrumental for identity verification and is now extensively utilized in criminal investigations, voice biometrics, and customer service sectors. Recognizing speakers irrespective of spoken content, ResNet-based architectures adeptly extract speaker embeddings by implementing residual connections in convolutional networks and standardizing residual blocks. However, their efficacy diminishes when confronted with complex input feature spaces. To mitigate these challenges, various Feature extraction variants have been investigated using customized residual learning approach. In context of speech processing tasks, Thin-ResNet, ResNet MSE, and ResNet-based TDNN have demonstrated superior performance compared to conventional speech processing methods. In this work, Res42Net model introduces extraction of Mel-filters, Sinc Band-pass filters and Bark-Frequency psychoacoustic features directly from the audio signals as input data, ensuring significant enhancement in the model’s speakers’ representation. This paper explores and implements Res42Net model trained on three cepstral features using an open-source dataset of Hindi speakers. The proposed system is evaluated and compared against the Res2Net-based system and EER reports the superior performance of the proposed system over the existing models.