Pinhole Effect on Linkability and Dispersion in Speaker Anonymization
Kong Aik Lee, Zeyan Liu, Liping Chen, Zhen-Hua Ling · IEEE Signal Processing Letters · 2025
Speaker anonymization aims to conceal speaker-specific attributes in speech signals, making the anonymized speech unlinkable to the original speaker identity. Recent approaches achieve this by disentangling speech into content and speaker components, replacing the latter with pseudo- speakers. The anonymized speech can be mapped either to a common pseudo-speaker shared across instances or to distinct pseudo-speakers unique to each instance. This paper investigates the impact of these mapping strategies on three key dimensions: speaker linkability, dispersion in the anonymized speaker space, and de-identification from the original identity. Our findings show that using distinct pseudo-speakers increases speaker dispersion and reduces linkability compared to common pseudo-speaker mapping, while maintaining de-identification, thereby enhancing overall privacy preservation. These observations are interpreted through the proposedpinhole effect, a conceptual framework introduced to explain the relationship between mapping strategies and anonymization performance. The hypothesis is validated through empirical evaluation.