The social media revolution has produced a plethora of web services to which users can easily upload and share multimedia documents. Despite the popularity and convenience of such services, the sharing of such inherently personal data, including speech data, raises obvious security and privacy concerns. In particular, a user's speech data may be acquired and used with speech synthesis systems to produce high-quality speech utterances which reflect the same user's speaker identity. These utterances may then be used to attack speaker verification systems. One solution to mitigate these concerns involves the concealing of speaker identities before the sharing of speech data. For this purpose, we present a new approach to speaker anonymization. The idea is to extract linguistic and speaker identity features from an utterance and then to use these with neural acoustic and waveform models to synthesize anonymized speech. The original speaker identity, in the form of timbre, is suppressed and replaced with that of an anonymous pseudo identity. The approach exploits state-of-the-art x-vector speaker representations. These are used to derive anonymized pseudo speaker identities through the combination of multiple, random speaker x-vectors. Experimental results show that the proposed approach is effective in concealing speaker identities. It increases the equal error rate of a speaker verification system while maintaining high quality, anonymized speech.
First of all, speaker anonymization differs from speech anonymization @cite_21 @cite_7 in that the former suppresses speaker identity while the latter obscures linguistic content. In accordance with the manipulation objectives, speaker anonymization can be split into physical and logical anonymization. Physical anonymization aims to perturb speech in physical space by adding an external sound to the original waveform @cite_24 while logical anonymization modifies speaker identity on the recorded speech signal. Our proposed method falls into the latter category.
@cite_24 @cite_21 @cite_7
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In this paper, a privacy protection method to prevent speaker identification from recorded speech is proposed and evaluated. Although many techniques for preserving various private information included in speech have been proposed, their impacts on human speech communication in physical space are not taken into account. To overcome this problem, this paper proposes privacy-preserving sound as a privacy protection method. The privacy-preserving sound can degrade speaker verification performance without interfering with human speech communication in physical space. To make a first step toward solving this problem, suitable sound characteristics for preserving privacy are evaluated in terms of the speaker verification performance and speech intelligibility. The experimental results show that appropriate sound can efficiently degrade the speaker verification performance without degrading speech intelligibility. Privacy protection issue introduces numerous challenges in the multimedia processing domain. In this paper, we propose an anonymization framework for audio clinical data. The HMM based keyword recognition technique is used to locate the predefined sensitive keywords, which are identified by the users or patients in advance. These keywords will then be substituted by the synthesized nominal words of the similar nature and voice characteristics. The ultimate goal is to protect the privacy information as much as possible, while trying to preserve the speech properties, especially the disease-related symptoms, such as the loudness, the rhythm, the emotion, etc. A preliminary system is presented to demonstrate the usage of the process.