Exploiting Context-dependent Duration Features for Voice Anonymization Attack Systems
Fuente:
arXiv
Salvato in:
| Autori principali: | , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2025
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866915401497051136 |
|---|---|
| author | Tomashenko, Natalia Vincent, Emmanuel Tommasi, Marc |
| author_facet | Tomashenko, Natalia Vincent, Emmanuel Tommasi, Marc |
| contents | The temporal dynamics of speech, encompassing variations in rhythm, intonation, and speaking rate, contain important and unique information about speaker identity. This paper proposes a new method for representing speaker characteristics by extracting context-dependent duration embeddings from speech temporal dynamics. We develop novel attack models using these representations and analyze the potential vulnerabilities in speaker verification and voice anonymization systems.The experimental results show that the developed attack models provide a significant improvement in speaker verification performance for both original and anonymized data in comparison with simpler representations of speech temporal dynamics reported in the literature. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_15214 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Exploiting Context-dependent Duration Features for Voice Anonymization Attack Systems Tomashenko, Natalia Vincent, Emmanuel Tommasi, Marc Sound Computation and Language Cryptography and Security Audio and Speech Processing The temporal dynamics of speech, encompassing variations in rhythm, intonation, and speaking rate, contain important and unique information about speaker identity. This paper proposes a new method for representing speaker characteristics by extracting context-dependent duration embeddings from speech temporal dynamics. We develop novel attack models using these representations and analyze the potential vulnerabilities in speaker verification and voice anonymization systems.The experimental results show that the developed attack models provide a significant improvement in speaker verification performance for both original and anonymized data in comparison with simpler representations of speech temporal dynamics reported in the literature. |
| title | Exploiting Context-dependent Duration Features for Voice Anonymization Attack Systems |
| topic | Sound Computation and Language Cryptography and Security Audio and Speech Processing |
| url | https://arxiv.org/abs/2507.15214 |