Please check whether this paper is about 'Voice Conversion' or not.
article info.
title: Anonymising Elderly and Pathological Speech: Voice Conversion Using DDSP and Query-by-Example
summary: 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.
Please check whether this paper is about 'Voice Conversion' or not.
article info.
title: Anonymising Elderly and Pathological Speech: Voice Conversion Using DDSP and Query-by-Example
summary: 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.
id: http://arxiv.org/abs/2410.15500v1
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