A Classification Retrieval Method for Encrypted Speech Based on Deep Neural Network and Deep Hashing

• Published in: IEEE access Vol. 8; pp. 202469 - 202482
• Main Authors: Zhang, Qiuyu, Zhao, Xuejiao, Hu, Yingjie
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Artificial neural networks; Binary codes; Classification; Coding; Cryptography; deep hashing; deep neural network; Encrypted speech retrieval; Feature extraction; Hash based algorithms; Log-Mel spectrogram; Machine learning; Neural networks; Recurrent neural networks; Retrieval; Semantics; Spectrogram; Speech; speech classification; Speech coding; Speech recognition
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2020.3036048

In order to improve the retrieval efficiency and accuracy of the existing encrypted speech retrieval methods, and improve the semantic representation of speech features and classification performance, a classification retrieval method for encrypted speech based on deep neural network (DNN) and deep hashing is proposed. Firstly, the speech files are classified according to the category tags, and the speech files are encrypted by Rossler chaotic map method and uploaded to the cloud encrypted speech library. Secondly, the Log-Mel spectrogram features of the original speech are extracted, and extract deep semantic features and generate classification results through the trained convolutional neural network (CNN) and convolutional recurrent neural network (CRNN). Finally, the semantic feature hash code is obtained through the constructed hash function, combined with the category hash code encoded by One Hot coding to obtain the final deep hashing binary code, and uploaded to the deep hashing index table. When retrieval, the deep hashing binary code of the query speech is obtained, and the "two-stage" classification retrieval strategy and the normalized Hamming distance algorithm are used to match the semantic feature hash. Experimental results show that the proposed two DNN coding models have excellent feature learning performance, and has better recall rate, precision rate and retrieval efficiency.