Natural language processing is a very active area of research and development, there is not a single agreed upon a method that would satisfy everyone for the use of natural language to operate electronic devices or other practical applications. But there are some aspects used from many years in the formulation and solution of computational problem arising in natural language processing. This paper describes a model in which numerical values are assigned to word of natural language speech data set to convert the information present in natural language speech data set into an intermediate numeric form as a structured data set. The intermediated numerical values of each word will be used for generation of machine code which will be easily understand by electronic devices to draw inferences from data set. The designed model is useful for a number of practical applications and very simple to implement.
The presented paper proposes a new speech command recognition model for novel engineering applications with limited resources. We built the proposed model with the help of a Convolutional Recurrent Neural Network (CRNN). The use of CRNN instead of Convolutional Neural Network (CNN) helps us to reduce the model parameters and memory requirement as per resource constraints. Furthermore, we insert transmute and curtailment layer between the layers of CRNN. By doing this we further reduce model parameters and float number of operations to half of the CRNN requirement. The proposed model is tested on Google’s speech command dataset. The obtained result shows that the proposed CRNN model requires 1/3 parameters as compared to the CNN model. The number of parameters of the CRNN model is further reduced by 45% and the float numbers of operations between 2% to 12 % in different recognition tasks. The recognition accuracy of the proposed model is 96% on Google’s speech command dataset, and on laboratory recording, its recognition accuracy is 89%.
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