摘要
本研究旨在探讨一种基于时频特征设计的反监听技术,重点研究如何通过动态修改时序和频率增强特定频率范围内的人类语音干扰。本文针对现有的语音干扰技术展开研究,并与标准噪声注入方法进行了比较。研究方法包括理论分析和实验验证,通过对实际原型进行测试和验证,评估了基于时频特征提取的干扰信号在干扰语音识别系统方面的有效性。实验结果显示,当信噪比低于0dB时,所提出方法的文本识别错误率超过了60%;而当信噪比为0dB时,本文算法的文本识别错误率平均比当前干扰算法高出20%以上。此外,当干扰系统与录音设备保持相同距离时,本文算法在录音设备上产生的信噪比比当前算法低近2dB,这说明了所提出算法的高能量利用效率。因此,本研究成果对于提高通信安全和保护隐私具有重要意义,特别是在需要高度保密的通信环境中。
This study aimed to explore an anti-eavesdropping technique based on time-frequency feature design,focusing on how to dynamically modify the temporal and frequency aspects to enhance human speech interference within specific frequency ranges.The paper conducted research on existing speech interference techniques,comparing them with standard noise injection methods.The research methods included theoretical analysis and experimental validation.By testing and evaluating the interference signals based on time-frequency feature extraction on an actual prototype,the effectiveness of the interference in disrupting speech recognition systems was assessed.The experimental results showed that when the signal-to-noise ratio(SNR)was lower than 0 dB,the proposed method's text recognition error rate(WER)was over 60%.Moreover,when the SNR was 0 dB,the WER of the algorithm in this paper was higher than that of current jamming algorithms by more than 20%on average.Additionally,when the jamming system maintained the same distance from the recording device,the SNR produced by this paper's algorithm on the recording device was lower than that of the current algorithm by almost 2 dB.This demonstrates the high energy utilization efficiency of the proposed algorithm.Therefore,the findings of this research have significant implications for improving communication security and protecting privacy,especially in environments that require a high level of confidentiality.
作者
李建勋
王开
Li Jianxun;Wang Kai(School of Information Science and Engineering,Southeast University,Nanjing 211189,China)
出处
《电子测量技术》
北大核心
2024年第7期1-8,共8页
Electronic Measurement Technology
基金
国家自然科学基金(62234012)项目资助
关键词
隐私保护
防窃听
超声波干扰
时频特征提取
语音干扰
privacy protection
anti-eavesdropping
ultrasonic interference
time-frequency feature extraction
speech interference
