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.
With a goal closely related to that of anonymization, @cite_15 investigated so-called speaker evasion and obfuscation using voice conversion techniques. With the work aiming only to circumvent surveillance systems, it evaluated only how the approach could degrade ASV performance. It did not consider degradations to speech quality. In contrast, the ideas presented in this paper are evaluated in terms of speaker identity anonymization, speech quality and linguistic content.
@cite_15
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The potential for biometric systems to be manipulated through some form of subversion is well acknowledged. One such approach known as spoofing relates to the provocation of false accepts in authentication applications. Another approach referred to as obfuscation relates to the provocation of missed detections in surveillance applications. While the automatic speaker verification research community is now addressing spoofing and countermeasures, vulnerabilities to obfuscation remain largely unknown. This paper reports the first study. Our work with standard NIST datasets and protocols shows that the equal error rate of a standard GMM-UBM system is increased from 9 to 48 through obfuscation, whereas that of a state-of-the-art i-vector system increases from 3 to 20 . We also present a generalised approach to obfuscation detection which succeeds in detecting almost all attempts to evade detection.