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An Encrypted Speech Retrieval Method Based on Deep Perceptual Hashing and CNN-BiLSTM | |
Zhang, Qiuyu; Li, Yuzhou; Hu, Yingjie; Zhao, Xuejiao | |
2020 | |
发表期刊 | IEEE Access |
ISSN | 2169-3536 |
卷号 | 8页码:148556-148569 |
摘要 | Since convolutional neural network (CNN) can only extract local features, and long short-term memory (LSTM) neural network model has a large number of learning calculations, a long processing time and an obvious degree of information loss as the length of speech increases. Utilizing the characteristics of autonomous feature extraction in deep learning, CNN and bidirectional long short-term memory (BiLSTM) network are combined to present an encrypted speech retrieval method based on deep perceptual hashing and CNN-BiLSTM. Firstly, the proposed method extracts the Log-Mel Spectrogram/MFCC features of the original speech and enters the CNN and BiLSTM networks in turn for model training. Secondly, we use the trained fusion network model to learn the deep perceptual feature and generate deep perceptual hashing sequences. Finally, the normalized Hamming distance algorithm is used for matching retrieval. In order to protect the speech security in the cloud, a speech encryption algorithm based on a 4D hyperchaotic system is proposed. The experimental results show that the proposed method has good discrimination, robustness, recall and precision compared with the existing methods, and it has good retrieval efficiency and retrieval accuracy for longer speech. Meanwhile, the proposed speech encryption algorithm has a high key space to resist exhaustive attacks. © 2013 IEEE. |
关键词 | Bismuth compounds Brain Convolutional neural networks Cryptography Deep learning Hamming distance Information retrieval Learning systems Speech Distance algorithm Hyper-chaotic systems Neural network model Perceptual feature Perceptual hashing Recall and precision Retrieval accuracy Retrieval efficiency |
DOI | 10.1109/ACCESS.2020.3015876 |
收录类别 | SCI ; SCIE ; EI |
语种 | 英语 |
WOS研究方向 | Computer Science ; Engineering ; Telecommunications |
WOS类目 | Computer Science, Information Systems ; Engineering, Electrical & Electronic ; Telecommunications |
WOS记录号 | WOS:000562065100001 |
出版者 | Institute of Electrical and Electronics Engineers Inc. |
EI入藏号 | 20203709158786 |
EI主题词 | Long short-term memory |
EI分类号 | 461.1 Biomedical Engineering - 751.5 Speech - 903.3 Information Retrieval and Use |
来源库 | Compendex |
分类代码 | 461.1 Biomedical Engineering - 751.5 Speech - 903.3 Information Retrieval and Use |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | https://ir.lut.edu.cn/handle/2XXMBERH/114980 |
专题 | 计算机与通信学院 |
通讯作者 | Zhang, Qiuyu |
作者单位 | Lanzhou Univ Technol, Sch Comp & Commun, Lanzhou 730050, Peoples R China |
第一作者单位 | 兰州理工大学 |
通讯作者单位 | 兰州理工大学 |
第一作者的第一单位 | 兰州理工大学 |
推荐引用方式 GB/T 7714 | Zhang, Qiuyu,Li, Yuzhou,Hu, Yingjie,et al. An Encrypted Speech Retrieval Method Based on Deep Perceptual Hashing and CNN-BiLSTM[J]. IEEE Access,2020,8:148556-148569. |
APA | Zhang, Qiuyu,Li, Yuzhou,Hu, Yingjie,&Zhao, Xuejiao.(2020).An Encrypted Speech Retrieval Method Based on Deep Perceptual Hashing and CNN-BiLSTM.IEEE Access,8,148556-148569. |
MLA | Zhang, Qiuyu,et al."An Encrypted Speech Retrieval Method Based on Deep Perceptual Hashing and CNN-BiLSTM".IEEE Access 8(2020):148556-148569. |
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