Online speaker de-identification using voice transformation

Pobar, Miran; Ipšić, Ivo

doi:10.1109/mipro.2014.6859761

Cited by 27 publications

(18 citation statements)

References 5 publications

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“…For speech, several classic approaches for speaker anonymization are explored in [31]- [34], while a neural-network-based encoder-decoder approach has been proposed in [35].…”

Section: Cyber Worldmentioning

confidence: 99%

Preventing Fake Information Generation Against Media Clone Attacks

Babaguchi

Echizen

Yamagishi

et al. 2021

IEICE Trans. Inf. & Syst.

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“…For speech, several classic approaches for speaker anonymization are explored in [31]- [34], while a neural-network-based encoder-decoder approach has been proposed in [35].…”

Section: Cyber Worldmentioning

confidence: 99%

Preventing Fake Information Generation Against Media Clone Attacks

Babaguchi

Echizen

Yamagishi

et al. 2021

IEICE Trans. Inf. & Syst.

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“…Both layers are able to identify the speaker and therefore to be anonymized. Advanced voice-based speaker anonymization aims to suppress the speaker identity and is mostly based on voice transformation techniques, changing source, or filter characteristics of the speech (Pobar & Ipšić, 2014). Recent research suggested to use x-vector speaker representations to suppress the timbre of a speaker, and thus hindering the speaker identification (Fang et al, 2019).…”

Section: Anonymization and De-anonymization Techniquesmentioning

confidence: 99%

Personal data protection and academia: GDPR issues and multi-modal data-collections "in the wild"

Siegert

Silber-Varod

Carmi

et al. 2020

OJAKM

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The European Union (EU) General Data Protection Regulations (GDPR) has a direct impact on research activities, as it raises the awareness of personal rights not only among the scientists but also among the data-subjects scientists process information from. This paper presents the dilemma related to the privacy of audio and video data, compliance with the EU GDPR, and techniques to anonymize and pseudonymize such data. We further discuss issues of “in the wild” personal data collection by focusing on multi-modal collections, mainly of audio, video via these channels. Throughout this paper we define relevant core issues and highlight two challenges of “in the wild” data collection: Internet crawling and public data collecting. In the last section, some exemplary use cases are demonstrating the raised issues, illuminating how GDPR affects the collection of publicly available data; how privacy concerns influence participant behavior, and which de-anonymization levels can be reached with what kind of data. The key point we present is that the identity of the participants is revealed in the voice or video signal, while the latter is at the same time the object of the research. One implication is that the research community has to actively disconnect the data from the personal information on the participants. Hence the importance of a process of anonymity or omission of data for research activity. This entail the development of an infrastructure for data access control to enable data sharing among researchers

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“…A novel scheme for voice de-identification, where a set of precalculated voice transformations based on GMM mapping is used to de-identify the speech of a new speaker, is presented in [155]. The scheme enables the online de-identification of speakers whose speech has not been used in the training phase to build a voice transformation.…”

Section: Voice De-identificationmentioning

confidence: 99%

De-identification for privacy protection in multimedia content: A survey

Ribarić

Ariyaeeinia

Pavešić

2016

Signal Processing: Image Communication

152

107

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a b s t r a c tPrivacy is one of the most important social and political issues in our information society, characterized by a growing range of enabling and supporting technologies and services. Amongst these are communications, multimedia, biometrics, big data, cloud computing, data mining, internet, social networks, and audio-video surveillance. Each of these can potentially provide the means for privacy intrusion. Deidentification is one of the main approaches to privacy protection in multimedia contents (text, still images, audio and video sequences and their combinations). It is a process for concealing or removing personal identifiers, or replacing them by surrogate personal identifiers in personal information in order to prevent the disclosure and use of data for purposes unrelated to the purpose for which the information was originally obtained. Based on the proposed taxonomy inspired by the Safe Harbour approach, the personal identifiers, i.e., the personal identifiable information, are classified as non-biometric, physiological and behavioural biometric, and soft biometric identifiers. In order to protect the privacy of an individual, all of the above identifiers will have to be de-identified in multimedia content. This paper presents a review of the concepts of privacy and the linkage among privacy, privacy protection, and the methods and technologies designed specifically for privacy protection in multimedia contents. The study provides an overview of de-identification approaches for non-biometric identifiers (text, hairstyle, dressing style, license plates), as well as for the physiological (face, fingerprint, iris, ear), behavioural (voice, gait, gesture) and soft-biometric (body silhouette, gender, age, race, tattoo) identifiers in multimedia documents.

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Online speaker de-identification using voice transformation

Cited by 27 publications

References 5 publications

Preventing Fake Information Generation Against Media Clone Attacks

Preventing Fake Information Generation Against Media Clone Attacks

Personal data protection and academia: GDPR issues and multi-modal data-collections "in the wild"

De-identification for privacy protection in multimedia content: A survey

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