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

arXiv: https://arxiv.org/abs/2412.17164 · HTML: https://mlsanity.com/paper/2412.17164/

## 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 (most cited first, 17 of 17)

- [Robust Speech Recognition via Large-Scale Weak Supervision](https://mlsanity.com/paper/2212.04356/) (2022)
- [LibriTTS: A Corpus Derived from LibriSpeech for Text-to-Speech](https://mlsanity.com/paper/1904.02882/) (2019)
- [ECAPA-TDNN: Emphasized Channel Attention, Propagation and Aggregation in TDNN Based Speaker Verification](https://mlsanity.com/paper/2005.07143/) (2020)
- [Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech](https://mlsanity.com/paper/2106.06103/) (2021)
- [Introducing the VoicePrivacy Initiative](https://mlsanity.com/paper/2005.01387/) (2020)
- [The VoicePrivacy 2020 Challenge: Results and findings](https://mlsanity.com/paper/2109.00648/) (2021)
- [Speaker Anonymization Using X-vector and Neural Waveform Models](https://mlsanity.com/paper/1905.13561/) (2019)
- [The VoicePrivacy 2024 Challenge Evaluation Plan](https://mlsanity.com/paper/2404.02677/) (2024)
- [Speaker anonymisation using the McAdams coefficient](https://mlsanity.com/paper/2011.01130/) (2020)
- [Anonymizing Speech: Evaluating and Designing Speaker Anonymization Techniques](https://mlsanity.com/paper/2308.04455/) (2023)
- [Speaker anonymization using neural audio codec language models](https://mlsanity.com/paper/2309.14129/) (2023)
- [Speaker anonymization using orthogonal Householder neural network](https://mlsanity.com/paper/2305.18823/) (2023)
- [Language-Independent Speaker Anonymization Approach using Self-Supervised Pre-Trained Models](https://mlsanity.com/paper/2202.13097/) (2022)
- [Improving speaker de-identification with functional data analysis of f0 trajectories](https://mlsanity.com/paper/2203.16738/) (2022)
- [MUSA: Multi-lingual Speaker Anonymization via Serial Disentanglement](https://mlsanity.com/paper/2407.11629/) (2024)
- [HLTCOE JHU Submission to the Voice Privacy Challenge 2024](https://mlsanity.com/paper/2409.08913/) (2024)
- [Speech Rhythm-Based Speaker Embeddings Extraction from Phonemes and Phoneme Duration for Multi-Speaker Speech Synthesis](https://mlsanity.com/paper/2402.07085/) (2024)

## Built on by (most cited first, 5 of 5)

- [The Third VoicePrivacy Challenge: Preserving Emotional Expressiveness and Linguistic Content in Voice Anonymization](https://mlsanity.com/paper/2601.11846/) (2026)
- [Exploiting Context-dependent Duration Features for Voice Anonymization Attack Systems](https://mlsanity.com/paper/2507.15214/) (2025)
- [SegReConcat: A Data Augmentation Method for Voice Anonymization Attack](https://mlsanity.com/paper/2508.18907/) (2025)
- [Evaluating Identity Leakage in Speaker De-Identification Systems](https://mlsanity.com/paper/2508.14012/) (2025)
- [Beyond EER: Multi-Dimensional Evaluation of Information Leakage in Speaker De-Identification](https://mlsanity.com/paper/2609.18673/) (2026)

## 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.