基于迁移学习优化的DCNN语音识别技术  

DCNN speech recognition technology based on transfer learning optimization

作　　者：张安安[1] 邓芳明 ZHANG An’an;DENG Fangming(Institute of Energy Research,Jiangxi Academy of Sciences,Nanchang 330039,China;School of Electrical and Automation Engineering,East China Jiaotong University,Nanchang 330013,China)

机构地区：[1]江西省科学院能源研究所,江西南昌330039 [2]华东交通大学电气与自动化工程学院,江西南昌330013

出　　处：《现代电子技术》2020年第17期69-71,76,共4页Modern Electronics Technique

基　　金：国家自然科学基金(51767006);江西省重点研发计划(20181BBE50019);江西省应用研究培育计划(20181BBE58015);江西省教育厅科学技术项目(GJJ170378)。

摘　　要：针对现有语音识别技术识别精准度低的问题,提出一种基于深度卷积神经网络算法与迁移学习相结合的语音识别技术。由于深度卷积神经网络应用范围有限,当输入输出参数发生变化时,需要重新开始构建,体系结构训练时间过长,因此,采用迁移学习方法有利于降低数据集规模。仿真实验结果表明,迁移学习不仅适用于源数据集与迁移问题的目标数据集比较,而且也适用于两种不同数据集情况,小数据集应用不仅有利于降低数据集生成时间和费用,而且有利于降低模型培训时间和对计算能力的要求。Since the recognition accuracy of existing speech recognition technology is low,a speech recognition technology based on deep convolution neural network algorithm is proposed. Due to the limited application scope of deep convolutional neural network(DCNN),when the input and output parameters change,the deep convolution neural network needs to be rebuilt and the training duration of architecture is time-consuming. Therefore,the migration learning method is adopted,which is beneficial to the reduction of the data set scale. The results of simulation experiments show that the migration learning is not only suitable for comparing the source data set with the target data set of migration problem,but also suitable for situations of two different data sets. The application of small data sets is favorable to the reduction of not only the time and cost of data set generation,but also the training duration and computational ability requirement of the model.

关 键 词：语音识别 深度卷积神经网络 迁移学习 数据集规模 识别精度 培训时间 

分 类 号：TN912.34-34[电子电信—通信与信息系统] TN925[电子电信—信息与通信工程]