Improving speaker de-identification with functional data analysis of f0 trajectories

• URL: http://arxiv.org/abs/2203.16738v1
• Date: Thu, 31 Mar 2022 01:34:15 GMT
• Title: Improving speaker de-identification with functional data analysis of f0 trajectories
• Authors: Lauri Tavi, Tomi Kinnunen, Rosa Gonz\'alez Hautam\"aki
• Abstract summary: Formant modification is a simpler, yet effective method for speaker de-identification which requires no training data. This study introduces a novel speaker de-identification method, which, in addition to simple formant shifts, manipulates f0 trajectories based on functional data analysis. The proposed speaker de-identification method will conceal plausibly identifying pitch characteristics in a phonetically controllable manner and improve formant-based speaker de-identification up to 25%.
• Score: 10.809893662563926
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Due to a constantly increasing amount of speech data that is stored in different types of databases, voice privacy has become a major concern. To respond to such concern, speech researchers have developed various methods for speaker de-identification. The state-of-the-art solutions utilize deep learning solutions which can be effective but might be unavailable or impractical to apply for, for example, under-resourced languages. Formant modification is a simpler, yet effective method for speaker de-identification which requires no training data. Still, remaining intonational patterns in formant-anonymized speech may contain speaker-dependent cues. This study introduces a novel speaker de-identification method, which, in addition to simple formant shifts, manipulates f0 trajectories based on functional data analysis. The proposed speaker de-identification method will conceal plausibly identifying pitch characteristics in a phonetically controllable manner and improve formant-based speaker de-identification up to 25%.

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