An Efficient Voice Authentication System using Enhanced Inceptionv3 Algorithm

Automatic voice authentication based on deep learning is a promising technology that has received much attention from academia and industry. It has proven to be effective in a variety of applications, including biometric access control systems. Using biometric data in such systems is difficult, particularly in a centralized setting. It introduces numerous risks, such as information disclosure, unreliability, security, privacy, etc. Voice authentication systems are becoming increasingly important in solving these issues. This is especially true if the device relies on voice commands from the user. This work investigates the development of a text-independent voice authentication system. The spatial features of the voiceprint (corresponding to the speech spectrum) are present in the speech signal as a result of the spectrogram, and the weighted wavelet packet cepstral coefficients (W-WPCC) are effective for spatial feature extraction (corresponding to the speech spectrum). W- WPCC characteristics are calculated by combining sub-band energies with sub-band spectral centroids using a weighting scheme to generate noise-resistant acoustic characteristics. In addition, this work proposes an enhanced inception v3 model for voice authentication. The proposed InceptionV3 system extracts feature from input data from the convolutional and pooling layers. By employing fewer parameters, this architecture reduces the complexity of the convolution process while increasing learning speed. Following model training, the enhanced Inception v3 model classifies audio samples as authenticated or not based on extracted features. Experiments were carried out on the speech of five English speakers whose voices were collected from YouTube. The results reveal that the suggested improved method, based on enhanced Inception v3 and trained on speech spectrogram pictures, outperforms the existing methods. The approach generates tests with an average categorization accuracy of 99%. Compared to the performance of these network models on the given dataset, the proposed enhanced Inception v3 network model achieves the best results regarding model training time, recognition accuracy, and stability.