Privacy versus Emotion Preservation Trade-offs in Emotion-Preserving Speaker Anonymization
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , , , , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2024
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866909306752860160 |
|---|---|
| author | Cai, Zexin Xinyuan, Henry Li Garg, Ashi García-Perera, Leibny Paola Duh, Kevin Khudanpur, Sanjeev Andrews, Nicholas Wiesner, Matthew |
| author_facet | Cai, Zexin Xinyuan, Henry Li Garg, Ashi García-Perera, Leibny Paola Duh, Kevin Khudanpur, Sanjeev Andrews, Nicholas Wiesner, Matthew |
| contents | Advances in speech technology now allow unprecedented access to personally identifiable information through speech. To protect such information, the differential privacy field has explored ways to anonymize speech while preserving its utility, including linguistic and paralinguistic aspects. However, anonymizing speech while maintaining emotional state remains challenging. We explore this problem in the context of the VoicePrivacy 2024 challenge. Specifically, we developed various speaker anonymization pipelines and find that approaches either excel at anonymization or preserving emotion state, but not both simultaneously. Achieving both would require an in-domain emotion recognizer. Additionally, we found that it is feasible to train a semi-effective speaker verification system using only emotion representations, demonstrating the challenge of separating these two modalities. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_03655 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Privacy versus Emotion Preservation Trade-offs in Emotion-Preserving Speaker Anonymization Cai, Zexin Xinyuan, Henry Li Garg, Ashi García-Perera, Leibny Paola Duh, Kevin Khudanpur, Sanjeev Andrews, Nicholas Wiesner, Matthew Audio and Speech Processing Machine Learning Advances in speech technology now allow unprecedented access to personally identifiable information through speech. To protect such information, the differential privacy field has explored ways to anonymize speech while preserving its utility, including linguistic and paralinguistic aspects. However, anonymizing speech while maintaining emotional state remains challenging. We explore this problem in the context of the VoicePrivacy 2024 challenge. Specifically, we developed various speaker anonymization pipelines and find that approaches either excel at anonymization or preserving emotion state, but not both simultaneously. Achieving both would require an in-domain emotion recognizer. Additionally, we found that it is feasible to train a semi-effective speaker verification system using only emotion representations, demonstrating the challenge of separating these two modalities. |
| title | Privacy versus Emotion Preservation Trade-offs in Emotion-Preserving Speaker Anonymization |
| topic | Audio and Speech Processing Machine Learning |
| url | https://arxiv.org/abs/2409.03655 |