Abstract
Speech is one of the most private forms of personal communication. A sample of a person’s speech contains information about the gender, accent, ethnicity, and the emotional state of the speaker apart from the message content. Speech processing technology is widely used in biometric authentication in the form of speaker verification. In a conventional speaker verification system, the speaker patterns are stored without any obfuscation and the system matches the speech input obtained during authentication with these patterns. If the speaker verification system is compromised, an adversary can use these patterns to later impersonate the user.
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References
Pathak MA, Raj B (2011) Privacy preserving speaker verification using adapted GMMs. Interspeech, In, pp 2405–2408
Pathak MA, Raj B (2012a) Privacy preserving speaker verification and identification using gaussian mixture models. In: IEEE transactions on audio, speech and, language processing (to appear)
Pathak MA, Raj B (2012b) Privacy preserving speaker verification as password matching. In: IEEE international conference on acoustics, speech and signal processing
Pathak MA, Rane S, Sun W, Raj B (2011) Privacy preserving probabilistic inference with hidden Markov models. In: IEEE international conference on acoustics, speech and signal processing
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Pathak, M.A. (2013). Thesis Overview. In: Privacy-Preserving Machine Learning for Speech Processing. Springer Theses. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-4639-2_1
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DOI: https://doi.org/10.1007/978-1-4614-4639-2_1
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