Research On Developmental Network Model Imitating The Human Auditory System

With the popularization of artificial intelligence technology,robots have gradually entered the daily life of human beings.To understand the language of human beings for the robot is to enable the robot to hear like human beings.At present,the voice recognition is the core of the field of machine hearing.Compared with the machine hearing,the human auditory system can accurately and quickly identify the content of the sound.Therefore,the simulation of human auditory system has become a hot topic in the field of machine hearing.This paper introduces the developmental network to construct the auditory model by simulating the ventral path structure of human auditory system.The ventral pathway mainly deals with the content information of the voice,which is mainly responsible for the recognition of the content of the sound.Finally,in order to reflect the performance of the proposed model,this paper sets up speech recognition experiments to verify the performance.This paper is divided into three parts.Firstly,the structure of the human auditory system is briefly introduced,and the signal transduction pathways are analyzed in detail.Then the basic theory of closed skull development network is introduced,the autonomous development of thought are expounded,and the basic model and learning algorithm of network development are explained.Secondly,a network model based on the developmental network,is developed to simulate the ventral structure of human auditory system.This paper briefly introduces the characteristics of the development network model,and puts forward the improvement of the model structure.A variety of bionic mechanisms are introduced in the developmental network,such as the design of neuronal connections and learning state of neurons for the autonomous learning,the regeneration of neurons to make full use of neurons and increasing the release mechanism to solve the problem of resource allocation of neurons.The development program of the developmental network model is designed,and the speech dataset is created.Then the speechrecognition experiments are carried out to verify the performance of the network model.The experimental results show that the recognition rates of English words and phrases can reach 94.44% and 90.19% respectively.It shows that the model has the ability to recognize speech in real environment.Finally,in order to further improve the performance of development network model,the deep belief network is introduced to construct the feature extractor to extract the depth characteristics of speech.The feature representation of speech through deep belief network is analyzed,and the features are visualized.On the basis of the original speech database,combined with the new features,the speech recognition experiments of the development network model are carried out.It can be seen from the experimental results that the recognition rate of English words and phrases can be up to 96.30% and 92.96% respectively on the basis of the new features.The results show that the performance of the model has been greatly improved.And the developmental network model has better performance comparing with the traditional speech recognition model.