期刊名称：International Journal of Computer Trends and Technology
电子版ISSN：2231-2803
出版年度：2019
卷号：67
期号：4
页码：40-43
DOI：10.14445/22312803/IJCTT-V67I4P109
出版社：Seventh Sense Research Group
摘要：This paper mainly focuses on developing an efficient word recognition system by combining the good parameters of the SIFT incorporating it with the CNN structure. Several advancements are made in the Automatic Speech Recognition (ASR) technology that brings to ease the machine to understand the natural language. The main constrain rise is the nature of the input speech signal, which makes it difficult to retain the original information. This can be overcome by hybridizing the (SIFT) Scale Invariant Feature Transform with (CNN) Convolutional Neural Network architecture. The noisy speech signal is initially passed through the pre –processing stage and converted to the spectrogram to extract the feature. The extracted features are now fed to the layers of CNN in order to train the model. At the testing phase the vectors are now cross matched and the maximum close weighted value from the fully connected layers lead to the output. The system performs with an efficiency of 94.78% in nonisolated environment.
