mlsanityData to 14 Oct 2026

Analysis of Speech Temporal Dynamics in the Context of Speaker Verification and Voice Anonymization

arXiv 2412.17164 ↗First posted 22 Dec 2024eess.AS cs.CL cs.SDcited by 5

Not on the map. Why?

Abstract (arXiv)

In this paper, we investigate the impact of speech temporal dynamics in application to automatic speaker verification and speaker voice anonymization tasks. We propose several metrics to perform automatic speaker verification based only on phoneme durations. Experimental results demonstrate that phoneme durations leak some speaker information and can reveal speaker identity from both original and anonymized speech. Thus, this work emphasizes the importance of taking into account the speaker's speech rate and, more importantly, the speaker's phonetic duration characteristics, as well as the need to modify them in order to develop anonymization systems with strong privacy protection capacity.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 17 of its 29 references that are in the map. First posted on arXiv 22 Dec 2024.

Builds on

17 of 29 references.

References · 17

  1. 2022Robust Speech Recognition via Large-Scale Weak Supervisioncited by 4,057
  2. 2019LibriTTS: A Corpus Derived from LibriSpeech for Text-to-Speechcited by 649
  3. 2020ECAPA-TDNN: Emphasized Channel Attention, Propagation and Aggregation in TDNN Based Speaker Verificationcited by 628
  4. 2021Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speechcited by 561
  5. 2020Introducing the VoicePrivacy Initiativecited by 49
  6. 2021The VoicePrivacy 2020 Challenge: Results and findingscited by 46
  7. 2019Speaker Anonymization Using X-vector and Neural Waveform Modelscited by 41
  8. 2024The VoicePrivacy 2024 Challenge Evaluation Plancited by 41
  9. 2020Speaker anonymisation using the McAdams coefficientcited by 38
  10. 2023Anonymizing Speech: Evaluating and Designing Speaker Anonymization Techniquescited by 22
  11. 2023Speaker anonymization using neural audio codec language modelscited by 22
  12. 2023Speaker anonymization using orthogonal Householder neural networkcited by 18
5 more
  1. 2022Language-Independent Speaker Anonymization Approach using Self-Supervised Pre-Trained Modelscited by 14
  2. 2022Improving speaker de-identification with functional data analysis of f0 trajectoriescited by 12
  3. 2024MUSA: Multi-lingual Speaker Anonymization via Serial Disentanglementcited by 10
  4. 2024HLTCOE JHU Submission to the Voice Privacy Challenge 2024cited by 10
  5. 2024Speech Rhythm-Based Speaker Embeddings Extraction from Phonemes and Phoneme Duration for Multi-Speaker Speech Synthesiscited by 3

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 3; 2026: 2.

5 papers.

  1. 2026The Third VoicePrivacy Challenge: Preserving Emotional Expressiveness and Linguistic Content in Voice Anonymizationcited by 8
  2. 2025Exploiting Context-dependent Duration Features for Voice Anonymization Attack Systemscited by 3
  3. 2025SegReConcat: A Data Augmentation Method for Voice Anonymization Attackcited by 1
  4. 2025Evaluating Identity Leakage in Speaker De-Identification Systemscited by 0
  5. 2026Beyond EER: Multi-Dimensional Evaluation of Information Leakage in Speaker De-Identificationcited by 0