클라우드 음성 서비스에서의 적대적 노이즈 기반 화자 비식별화 기법

초록

Cloud-based speech recognition services provide high convenience and accessibility, leading to their widespread adoption across various applications. However, the processing of speech data on remote servers has raised growing concerns about potential privacy breaches stemming from the exposure of speaker information. Although numerous speaker de-identification techniques have been proposed to address this issue, the preservation of semantic information in speech has received relatively little attention. To achieve a balance between speaker de-identification and speech recognition performance, this study proposes an adversarial noise-based approach. The proposed approach is designed based on the differences in input processing between speaker and speech recognition systems. Experimental results show that the proposed method can achieve a meaningful trade-off between speaker de-identification performance and speech recognition performance, with a balance between the two observed particularly in the 10%–20% noise intensity range. These findings suggest that the proposed method can serve as a practical alternative for privacy-preserving speech security in cloud-based speech service environments.

키워드