Open AccessLow Latency Based Convolutional Recurrent Neural Network Model for Speech Command Recognition
Author(s) -
Chhayarani Ram Kinkar,
Yogendra Kumar Jain
Publication year - 2021
Publication title -
informacinės technologijos ir valdymas
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.286
H-Index - 19
eISSN - 2335-884X
pISSN - 1392-124X
DOI - 10.5755/j01.itc.50.4.27352
Subject(s) - computer science , recurrent neural network , convolutional neural network , latency (audio) , speech recognition , float (project management) , artificial neural network , artificial intelligence , telecommunications , marine engineering , engineering
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%.