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With the development of smart devices, such as the Amazon Echo and Apple's HomePod, speech data have become a new dimension of big data.
“K-anonymity: A model for protecting privacy,”
L. Sweeney, · 2002
Earlier work this paper cites.
“Calibrating noise to sensitivity in private data analysis,”
C. Dwork and et al., · 2006
Earlier work this paper cites.
“SVM based speaker verification using a GMM supervector kernel and NAP variability compensation,”
W. Campbell and et al., · 2006
Earlier work this paper cites.
“Connectionist temporal classification:labelling unsegmented sequence data with recurrent neural networks,”
A. Graves and et al., · 2006
Earlier work this paper cites.
“Robust de-anonymization of large sparse datasets,”
A. Narayanan and V. Shmatikov, · 2008
Earlier work this paper cites.
“A study of inter-speaker variability in speaker verification,”
P. Kenny and et al., · 2008
Earlier work this paper cites.
“Front-end factor analysis for speaker verification,”
N. Dehak and et al., · 2011
Earlier work this paper cites.
“Theoretical results on de-anonymization via linkage attacks,”
M. M. Merener, · 2012
Earlier work this paper cites.
“Broadening the scope of differential privacy using metrics,”
K. Chatzikokolakis and et al., · 2013
Earlier work this paper cites.
“Geo-indistinguishability: Differential privacy for location-based systems,”
M. E. Andres and et al., · 2013
Earlier work this paper cites.
“Deep neural networks for small footprint text-dependent speaker verification,”
E. Variani and et al., · 2014
Cited alongside, same era.
“The new wechat password,”
Tencent Inc., · 2015
Cited alongside, same era.
“Spoofing and countermeasures for speaker verification: A survey,”
Z. Wu and et al., · 2015
Cited alongside, same era.
“Speaker de-identification using diphone recognition and speech synthesis,”
T. Justin and et al., · 2015
Cited alongside, same era.
“LibriSpeech: an ASR corpus based on public domain audio books,”
V. Panayotov and et al., · 2015
Cited alongside, same era.
“A time delay neural network architecture for efficient modeling of long temporal contexts,”
V. Peddinti and et al., · 2015
Cited alongside, same era.
“Hidebehind: Enjoy voice input with voiceprint unclonability and anonymity,”
J. Qian and et al., · 2018
Later among the works it cites.
“X-vectors: Robust dnn embeddings for speaker recognition,”
D. Snyder and et al., · 2018
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“On deep speaker embeddings for text-independent speaker recognition,”
S. Novoselov and et al., · 2018
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“Espnet: End-to-end speech processing toolkit,”
S. Watanabe and et al., · 2018
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“Voxceleb2: Deep speaker recognition,”
J. Chung and et al.., · 2018
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“The GDPR & speech data: Reflections of legal and technology communities, first steps towards a common understanding,” 2019
A. Nautsch and et al., · 2019
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“The speakers in the wild (SITW) speaker recognition database,”
M. McLaren and et al., · 2016
Cited alongside, same era.
“Voice biometrics: Deep learning-based voiceprint authentication system,”
A. Boles and P. Rad, · 2017
Cited alongside, same era.
“Synthesizing obama: learning lip sync from audio,”
S. Suwajanakorn and et al., · 2017
Cited alongside, same era.
“Voxceleb: a large-scale speaker identification dataset,”
A. Nagrani and et al., · 2017
Cited alongside, same era.
“Evaluating voice conversion-based privacy protection against informed attackers,”
B. Srivastava and et al., · 2019
Later among the works it cites.
“Speaker anonymization using X-vector and neural waveform models,”
F. Fang and et al., · 2019
Later among the works it cites.
“Practical image obfuscation with provable privacy,”
L. Fan, · 2019
Later among the works it cites.
“Privacy-Preserving Adversarial Representation Learning in ASR: Reality or Illusion?,”
B. Srivastava and et al., · 2019
Later among the works it cites.