A focus module-based lightweight end-to-end CNN framework for voiceprint recognition

The process of identifying a spokesperson from a collection of subsequent time series data is referred to as speaker identification. Convolutional neural networks (CNNs) and deep neural networks are the two types of neural networks that are used in the majority of modern experimental approaches. This work presents a CNN model for speaker identification using a jump-connected one-dimensional convolutional neural network (1-D CNN) with a focus module (FM). The 1-D convolutional layer integrated with FM is employed in the presented model for speaker characteristic extraction and lessens heterogeneity in the temporal and spatial domains, allowing for quicker layer processing. Furthermore, the layered CNN hopping interconnection is employed to overcome the connectivity glitches, and a solution based on softmax loss and smooth L1-norm combined regulation is presented to increase efficiency. The recommended network model was evaluated using the ELSDSR, TIMIT, NIST, 16,000 PCM, and experimental audio datasets. According to experimental data, the equal error rate (EER) of end-to-end CNN for voiceprint identification is 9.02% higher than baseline approaches. In experiments, our proposed speaker recognition (SR) model, which we refer to as the deep FM-1D CNN, had a high recognition accuracy of 99.21%. Moreover, the observations demonstrate that the proposed network model is more robust than other models.