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.