| Summary: | Along with the development of science and technology, especially internet of things (IOT), IOT-related products increasingly contribute to improving people’s life. Among those products, it is impossible not to mention smarts city, internet of Vehicle devices and especially smart home, which are usually voice controlled. Therefore, voice processing technology is also in need of improvement. The article mainly focuses on processing human voice independently of text. In particular, Convolutional network (CNN) and Support Vector Machine (SVM) will be integrated to create Feature Building Machine. SVMs are often used in voice 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 voice recognition and is the foundation to develop devices for smart home. The experimental results, which was used in the standard Voxceleb database, demonstrate the superiority in sound recognition compared to traditional i-vector methods or other CNN methods.
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