文章基本信息

标题：Strong Robustness Hash Algorithm of Speech Perception Based on Tensor Decomposition Model
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作者：Yibo Huang ; QiuYu Zhang
期刊名称：Journal of Software Engineering
印刷版ISSN：1819-4311
电子版ISSN：2152-0941
出版年度：2017
卷号：11
期号：1
页码：22-31
DOI：10.3923/jse.2017.22.31
出版社：Academic Journals Inc., USA
摘要：Background: With constant progress in modern speech communication technologies, the technology of communication is becoming more and more important in the transmission of the mobile environment and the speech data is prone to be attacked by the noise or maliciously tampered. Existing speech authentication algorithms are inefficient, complicated and cannot meet the real-time requirements of speech communication in the mobile computing environment. Materials and Methods: In order to make the speech perception Hash algorithm has strong robustness and high efficiency of authentication under the common background noise, this study put forward a speech perception Hash algorithm based on the tensor reconstruction and the decomposition is proposed. This algorithm analyzes the speech perception feature from the 3D perspective and acquires each speech component wavelet packet decomposition. The MFCC and ΔMFCC feature of each speech component are extracted to constitute the speech feature tensor. The feature tensor is decomposed tensor decomposition to reduce the complexity of the feature tensor. Speech authentication is done by generating the Hash values through the feature of matrix quantification which use mid-value. Results: Experimental results showing that the proposed algorithm is robust for content to maintain operations. It is able to resist the attack of the background noise which is commonly heard during a communication. Also, the algorithm is highly efficiency in terms of arithmetic and is able to meet the real-time requirements of speech communication and complete the speech authentication quickly. Conclusion: Compared with common algorithms, this algorithm has better authentication performance, it can effectively improve accuracy, real-time performance and be able to control the tensor size as required. Its model building is flexible besides, it can realize the speech content authentication and speaker authentication thus, the algorithm has high practical value.