Automatic Recognition of Speaker Labels Using CNN-SVM Scheme

The most natural method of interaction is speech. To recognize the speaker, we employ a method known as “automatic speaker recognition,” which takes into account the speaker's voice's several characteristics, including pitch, timbre, tension, frequency, etc. Additionally, it communicates details regarding the manner in which voice is created and conveyed. The method is applicable to forensics, monitoring, and authenticity applications. The problem with the current approach is the vast number of features employed for voice recognition. In this work, we are planning to use support vector machine (SVM) as a classifier and reinforcement learning as a linear regression to recognize the speaker's voice purely based on a limited set of attributes selected from the convolutional neural network. Building a clear, unambiguous and delegated automatic spokesman identification organism is the major goal of this endeavor. We will only test our algorithm on a small voice dataset due to space restrictions. To increase accuracy, the system is trained and evaluated using a large number of numerical speech data samples. Here, 125 examples will be used to help identify each phrase. The NIST and ELSDSR speaker datasets were used to conduct an analysis of the hybrid network models that were suggested earlier. The findings of our experiments demonstrated that the CNN-SVM model that we proposed achieved a high recognition accuracy of 95% (avg). In the same vein, the findings show that the deep network model that was proposed is superior to other models in terms of its ability to correctly identify speakers.