Combining Deep Neural Network with SVM to Identify Used in IOT

Abstract

Along with the development of science and technology, especially one of internet of things (IOT), products related to IOT have improved human life these days. In IOT-related utility products, it is impossible not to mention devices for smarts city, self-driving cars and especially for smart home. Devices for smarts home are usually controlled by voice. Therefore, voice processing technology is also in need of improvement. Speech processing enhancement that ensures safety in the process helps smart home devices bring about an evolutionary change. In the article, we mainly focus on human voice processing independently od the text. Particularly, we will integrate Convolutional network(CNN) and Suport Vector Machine(SVM) to create a Feature Building Machine. SVMs are often used in speech and image classification, which accordingly is a critical and swift data sorter. The article analyzes the advantages of the combination Deep Neural Network (DNN) and SVMs in speech recognition and is the foundation to develop devices for smart home. The results of the experiment, which was used in the standard Voxcelb database, demonstrate the superiority in sound recognition compared to traditional i-vector methods or other CNN methods.