Privacy-aware speaker trait and multimodal features relationship analysis in job interviews

As the use of speech data for applications like emotion detection and health profiling grows, so do the privacy risks associated with voice recordings that can reveal sensitive speaker traits. This study investigates voice anonymization methods designed to protect speaker identity while maintaining essential speech characteristics for accurate trait inference, specifically within the context of job interviews. Our experiments show that while anonymization alters several acoustic parameters, the anonymized speech from signal processing-based methods remains suitable for overall trait assessment, with performance comparable to original speech. The phase vocoder-based method, in particular, offers modest privacy gains with an acceptable trade-off in utility, especially in scenarios with minimal attack vectors. In contrast, a neural audio codec-based method altered prosodic features critical for speaker trait estimation, slightly reducing performance in this specific task. Despite this, when carefully configured, this method provides greater privacy and generally preserves utility for speech recognition and quality assessment, even under semi-informed attack scenarios.