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NPU-NTU System for Voice Privacy 2024 Challenge

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arxiv 2409.04173 v2 pith:5MN3CYMS submitted 2024-09-06 eess.AS

Jixun Yao , Nikita Kuzmin , Qing Wang , Pengcheng Guo , Ziqian Ning , Dake Guo , Kong Aik Lee , Eng-Siong Chng
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Lei Xie
This is my paper
classification eess.AS
keywords speakeridentitydistillationsystemanonymizationcontentemotionlinguistic
verification ladder T0 review T1 audit T2 compute T3 formal
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Speaker anonymization is an effective privacy protection solution that conceals the speaker's identity while preserving the linguistic content and paralinguistic information of the original speech. To establish a fair benchmark and facilitate comparison of speaker anonymization systems, the VoicePrivacy Challenge (VPC) was held in 2020 and 2022, with a new edition planned for 2024. In this paper, we describe our proposed speaker anonymization system for VPC 2024. Our system employs a disentangled neural codec architecture and a serial disentanglement strategy to gradually disentangle the global speaker identity and time-variant linguistic content and paralinguistic information. We introduce multiple distillation methods to disentangle linguistic content, speaker identity, and emotion. These methods include semantic distillation, supervised speaker distillation, and frame-level emotion distillation. Based on these distillations, we anonymize the original speaker identity using a weighted sum of a set of candidate speaker identities and a randomly generated speaker identity. Our system achieves the best trade-off of privacy protection and emotion preservation in VPC 2024.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. SegReConcat: A Data Augmentation Method for Voice Anonymization Attack

    cs.SD 2025-08 conditional novelty 6.0 of 10

    SegReConcat, a word-shuffle-and-concatenate augmentation, improves attacker speaker verification against five of seven voice anonymization systems in the VPAC 2024 benchmark.

  2. Exploiting Context-dependent Duration Features for Voice Anonymization Attack Systems

    cs.SD 2025-07 conditional novelty 6.0 of 10

    A context-dependent encoding of phoneme durations identifies speakers far better than average-duration vectors and remains effective on anonymized speech without retraining on anonymized data.

  3. EASY: Emotion-aware Speaker Anonymization via Factorized Distillation

    eess.AS 2025-05 conditional novelty 5.0 of 10

    EASY separates speaker identity, linguistic content, and emotion through sequential factorized distillation, and reports better privacy and emotion preservation than prior VoicePrivacy 2024 systems.

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