Anonymising Elderly and Pathological Speech: Voice Conversion Using DDSP and Query-by-Example
This addresses the need for privacy-preserving remote health monitoring in specialized speech domains, representing a domain-specific incremental improvement.
The paper tackled the problem of speech anonymization for elderly and pathological speech, which current methods fail to adequately handle, and proposed a voice conversion method (DDSP-QbE) that significantly outperforms state-of-the-art in intelligibility, prosody, and domain preservation.
Speech anonymisation aims to protect speaker identity by changing personal identifiers in speech while retaining linguistic content. Current methods fail to retain prosody and unique speech patterns found in elderly and pathological speech domains, which is essential for remote health monitoring. To address this gap, we propose a voice conversion-based method (DDSP-QbE) using differentiable digital signal processing and query-by-example. The proposed method, trained with novel losses, aids in disentangling linguistic, prosodic, and domain representations, enabling the model to adapt to uncommon speech patterns. Objective and subjective evaluations show that DDSP-QbE significantly outperforms the voice conversion state-of-the-art concerning intelligibility, prosody, and domain preservation across diverse datasets, pathologies, and speakers while maintaining quality and speaker anonymity. Experts validate domain preservation by analysing twelve clinically pertinent domain attributes.