# Evaluation of Speaker Anonymization on Emotional Speech

arXiv: https://arxiv.org/abs/2305.01759 · HTML: https://mlsanity.com/paper/2305.01759/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 31 references that are in the map.
- First posted on arXiv 15 Apr 2023.

## Builds on (most cited first, 9 of 9)

- [Introducing the VoicePrivacy Initiative](https://mlsanity.com/paper/2005.01387/) (2020)
- [Speaker Anonymization Using X-vector and Neural Waveform Models](https://mlsanity.com/paper/1905.13561/) (2019)
- [F0-consistent many-to-many non-parallel voice conversion via conditional autoencoder](https://mlsanity.com/paper/2004.07370/) (2020)
- [Evaluating Voice Conversion-based Privacy Protection against Informed Attackers](https://mlsanity.com/paper/1911.03934/) (2019)
- [x-vectors meet emotions: A study on dependencies between emotion and speaker recognition](https://mlsanity.com/paper/2002.05039/) (2020)
- [The GDPR & Speech Data: Reflections of Legal and Technology Communities, First Steps towards a Common Understanding](https://mlsanity.com/paper/1907.03458/) (2019)
- [Unsupervised Representation Disentanglement using Cross Domain Features and Adversarial Learning in Variational Autoencoder based Voice Conversion](https://mlsanity.com/paper/2001.07849/) (2020)
- [Deep neural networks for emotion recognition combining audio and transcripts](https://mlsanity.com/paper/1911.00432/) (2019)
- [Emotionless: Privacy-Preserving Speech Analysis for Voice Assistants](https://mlsanity.com/paper/1908.03632/) (2019)

## Built on by (most cited first, 1 of 1)

- [The VoicePrivacy 2024 Challenge Evaluation Plan](https://mlsanity.com/paper/2404.02677/) (2024)

## Abstract (arXiv)

Speech data carries a range of personal information, such as the speaker's identity and emotional state. These attributes can be used for malicious purposes. With the development of virtual assistants, a new generation of privacy threats has emerged. Current studies have addressed the topic of preserving speech privacy. One of them, the VoicePrivacy initiative aims to promote the development of privacy preservation tools for speech technology. The task selected for the VoicePrivacy 2020 Challenge (VPC) is about speaker anonymization. The goal is to hide the source speaker's identity while preserving the linguistic information. The baseline of the VPC makes use of a voice conversion. This paper studies the impact of the speaker anonymization baseline system of the VPC on emotional information present in speech utterances. Evaluation is performed following the VPC rules regarding the attackers' knowledge about the anonymization system. Our results show that the VPC baseline system does not suppress speakers' emotions against informed attackers. When comparing anonymized speech to original speech, the emotion recognition performance is degraded by 15% relative to IEMOCAP data, similar to the degradation observed for automatic speech recognition used to evaluate the preservation of the linguistic information.