# SAIC: Integration of Speech Anonymization and Identity Classification

arXiv: https://arxiv.org/abs/2312.15190 · HTML: https://mlsanity.com/paper/2312.15190/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 36 references that are in the map.
- First posted on arXiv 23 Dec 2023.

## Builds on (most cited first, 27 of 27)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Masked Autoencoders Are Scalable Vision Learners](https://mlsanity.com/paper/2111.06377/) (2021)
- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units](https://mlsanity.com/paper/2106.07447/) (2021)
- [Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization](https://mlsanity.com/paper/1703.06868/) (2017)
- [VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training](https://mlsanity.com/paper/2203.12602/) (2022)
- [VoxCeleb: a large-scale speaker identification dataset](https://mlsanity.com/paper/1706.08612/) (2017)
- [Masked Autoencoders that Listen](https://mlsanity.com/paper/2207.06405/) (2022)
- [SSAST: Self-Supervised Audio Spectrogram Transformer](https://mlsanity.com/paper/2110.09784/) (2021)
- [Contrastive Audio-Visual Masked Autoencoder](https://mlsanity.com/paper/2210.07839/) (2022)
- [De-identification of Patient Notes with Recurrent Neural Networks](https://mlsanity.com/paper/1606.03475/) (2016)
- [One-shot Voice Conversion by Separating Speaker and Content Representations with Instance Normalization](https://mlsanity.com/paper/1904.05742/) (2019)
- [MAE-AST: Masked Autoencoding Audio Spectrogram Transformer](https://mlsanity.com/paper/2203.16691/) (2022)
- [Masked Spectrogram Modeling using Masked Autoencoders for Learning General-purpose Audio Representation](https://mlsanity.com/paper/2204.12260/) (2022)
- [Introducing the VoicePrivacy Initiative](https://mlsanity.com/paper/2005.01387/) (2020)
- [Demystifying Inter-Class Disentanglement](https://mlsanity.com/paper/1906.11796/) (2019)
- [Multitask Learning with Low-Level Auxiliary Tasks for Encoder-Decoder Based Speech Recognition](https://mlsanity.com/paper/1704.01631/) (2017)
- [DASGIL: Domain Adaptation for Semantic and Geometric-aware Image-based Localization](https://mlsanity.com/paper/2010.00573/) (2020)
- [FT2TF: First-Person Statement Text-To-Talking Face Generation](https://mlsanity.com/paper/2312.05430/) (2023)
- [Masked Modeling Duo: Learning Representations by Encouraging Both Networks to Model the Input](https://mlsanity.com/paper/2210.14648/) (2022)
- [Recent Advances in Vision Transformer: A Survey and Outlook of Recent Work](https://mlsanity.com/paper/2203.01536/) (2022)
- [Non-Adversarial Image Synthesis with Generative Latent Nearest Neighbors](https://mlsanity.com/paper/1812.08985/) (2018)
- [Differentially Private Speaker Anonymization](https://mlsanity.com/paper/2202.11823/) (2022)
- [Voice-Indistinguishability: Protecting Voiceprint in Privacy-Preserving Speech Data Release](https://mlsanity.com/paper/2004.07442/) (2020)
- [AV-MaskEnhancer: Enhancing Video Representations through Audio-Visual Masked Autoencoder](https://mlsanity.com/paper/2309.08738/) (2023)
- [Speech Pseudonymisation Assessment Using Voice Similarity Matrices](https://mlsanity.com/paper/2008.13144/) (2020)

## Built on by (most cited first, 7 of 7)

- [Differential Private Federated Transfer Learning for Mental Health Monitoring in Everyday Settings: A Case Study on Stress Detection](https://mlsanity.com/paper/2402.10862/) (2024)
- [Efflex: Efficient and Flexible Pipeline for Spatio-Temporal Trajectory Graph Modeling and Representation Learning](https://mlsanity.com/paper/2404.12400/) (2024)
- [VeTraSS: Vehicle Trajectory Similarity Search Through Graph Modeling and Representation Learning](https://mlsanity.com/paper/2404.08021/) (2024)
- [CrossGP: Cross-Day Glucose Prediction Excluding Physiological Information](https://mlsanity.com/paper/2404.10901/) (2024)
- [GluMarker: A Novel Predictive Modeling of Glycemic Control Through Digital Biomarkers](https://mlsanity.com/paper/2404.12605/) (2024)
- [Toward Short-Term Glucose Prediction Solely Based on CGM Time Series](https://mlsanity.com/paper/2404.11924/) (2024)
- [Adaptive Multimodal Person Recognition: A Robust Framework for Handling Missing Modalities](https://mlsanity.com/paper/2512.14961/) (2025)

## Abstract (arXiv)

Speech anonymization and de-identification have garnered significant attention recently, especially in the healthcare area including telehealth consultations, patient voiceprint matching, and patient real-time monitoring. Speaker identity classification tasks, which involve recognizing specific speakers from audio to learn identity features, are crucial for de-identification. Since rare studies have effectively combined speech anonymization with identity classification, we propose SAIC - an innovative pipeline for integrating Speech Anonymization and Identity Classification. SAIC demonstrates remarkable performance and reaches state-of-the-art in the speaker identity classification task on the Voxceleb1 dataset, with a top-1 accuracy of 96.1%. Although SAIC is not trained or evaluated specifically on clinical data, the result strongly proves the model's effectiveness and the possibility to generalize into the healthcare area, providing insightful guidance for future work.