Smart Home Language Recognition System Design Based on DNN-LSTM Model
In order to improve the accuracy and matching rate of the smart home language recognition system,a speech recognition system based on the DNN-LSTM model is designed by adding a short-term memory neural network(LSTM)structure to the first layer of the deep neural network(DNN)model and using infor-mation entropy to achieve acoustic training and language matching.The system is applied to speech recogni-tion,and the results show that the accuracy of Chinese and English acoustic model recognition is 96.6%,and the language matching accuracy is 95.8%.This system has certain practical value to improve the intelligent level of smart home.
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