Fetching the paper…
Reading the bibliography…
With the rapid development of deep learning techniques, the popularity of voice services implemented on various Internet of Things (IoT) devices is ever increasing.
Darpa timit acoustic-phonetic continous speech corpus cd-rom. nist speech disc 1-1.1
J. S. Garofolo, L. F. Lamel, W. M. Fisher, J. G. Fiscus, and D. S. Pallett · 1993
Earlier work this paper cites.
Vtln-based voice conversion
D. Sundermann and H. Ney · 2003
Earlier work this paper cites.
Nuance exec on iphone 4s, siri, and the future of speech, 2011
S. Wildstrom · 2011
Earlier work this paper cites.
Ted-lium: An automatic speech recognition dedicated corpus
A. Rousseau, P. Deléglise, and Y. Esteve · 2012
Earlier work this paper cites.
Glove: Global vectors for word representation
J. Pennington, R. Socher, and C. D. Manning · 2014
Earlier work this paper cites.
Personalized speech recognition for Internet of Things
M. Mehrabani, S. Bangalore, and B. Stern · 2015
Earlier work this paper cites.
Librispeech: An ASR corpus based on public domain audio books
V. Panayotov, G. Chen, D. Povey, and S. Khudanpur · 2015
Earlier work this paper cites.
Discovery of meaningful rules in time series
M. Shokoohi-Yekta, Y. Chen, B. Campana, B. Hu, J. Zakaria, and E. Keogh · 2015
Earlier work this paper cites.
Speech enhancement with LSTM recurrent neural networks and its application to noise-robust ASR
F. Weninger, H. Erdogan, W. S, V. E, J. Le Roux, J. R. Hershey, and B. Schuller · 2015
Earlier work this paper cites.
Regulation (eu) 2016/679 of the European Parliament and of the Council of 27 April 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data and repealing Directive 95/46/EC (general data protection regulation)
European Parliament and Council of the European Union · 2016
Earlier work this paper cites.
The right to be forgotten in the media: A data-driven study
M. Xue, G. Magno, E. Cunha, V. Almeida, and K. W. Ross · 2016
Earlier work this paper cites.
Understanding black-box predictions via influence functions
P. W. Koh and P. Liang · 2017
Earlier work this paper cites.
Toys that listen: A study of parents, children, and Internet-connected toys
E. McReynolds, S. Hubbard, T. Lau, A. Saraf, M. Cakmak, and F. Roesner · 2017
Earlier work this paper cites.
Amazon may give app developers access to Alexa audio recordings, 2017
S. Nick · 2017
Earlier work this paper cites.
Membership inference attacks against machine learning models
R. Shokri, M. S. Song, and V. Shmatikov · 2017
Earlier work this paper cites.
Auditing black-box models for indirect influence
P. Adler, C. Falk, S. A. Friedler, T. Nix, G. Rybeck, C. Scheidegger, B. Smith, and S. Venkatasubramanian · 2018
Earlier work this paper cites.
Beware of WeChat voice scams: “cloning” users after WeChat voice, 2018
CCTV · 2018
Earlier work this paper cites.
An automatic tamil speech recognition system by using bidirectional recurrent neural network with self-organizing map
S. Lokesh, P. K. Malarvizhi, M. D. Ramya, P. Parthasarathy, and C. Gokulnath · 2018
Cited alongside, same era.
Understanding membership inferences on well-generalized learning models
Y. Long, V. Bindschaedler, L. Wang, D. Bu, X. Wang, H. Tang, C. A. Gunter, and K. Chen · 2018
Cited alongside, same era.
Machine learning with membership privacy using adversarial regularization
M. Nasr, R. Shokri, and A. Houmansadr · 2018
Cited alongside, same era.
Exploring open-source deep learning ASR for speech-to-text TV program transcription
J. M. Perero-Codosero, J. Antón-Martín, D. T. Merino, E. L. Gonzalo, and L. A. Hernández-Gómez · 2018
Cited alongside, same era.
Aequitas: A bias and fairness audit toolkit
P. Saleiro, B. Kuester, L. Hinkson, J. London, A. Stevens, A. Anisfeld, K. T. Rodolfa, and R. Ghani · 2018
The audio auditor: Participant-level membership inference in voice-based IoT
Y. Miao, B. Z. H. Zhao, M. Xue, C. Chen, L. Pan, J. Zhang, D. Kaafar, and Y. Xiang · 2019
Closest in time.
Preserving privacy in speaker and speech characterisation
A. Nautsch, A. Jiménez, A. Treiber, J. Kolberg, C. Jasserand, E. Kindt, H. Delgado, M. Todisco, M. A. Hmani, A. Mtibaa, et al · 2019
Closest in time.
Speech sanitizer: Speech content desensitization and voice anonymization
J. Qian, H. Du, J. Hou, L. Chen, T. Jung, and X. Li · 2019
Closest in time.
The Pytorch-Kaldi speech recognition toolkit
M. Ravanelli, T. Parcollet, and Y. Bengio · 2019
Closest in time.
Ml-leaks: Model and data independent membership inference attacks and defenses on machine learning models
A. Salem, Y. Zhang, M. Humbert, P. Berrang, M. Fritz, and M. Backes · 2019
Closest in time.
41% of voice assistant users have concerns about trust and privacy, report finds, 2019
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Hey, Alexa, what can you hear? and what will you do with it?, 2018
M. Sapna · 2018
Cited alongside, same era.
Adversarial attacks against automatic speech recognition systems via psychoacoustic hiding
L. Schönherr, K. Kohls, S. Zeiler, T. Holz, and D. Kolossa · 2018
Cited alongside, same era.
End-to-end audio replay attack detection using deep convolutional networks with attention
F. Tom, M. Jain, and P. Dey · 2018
Cited alongside, same era.
Privacy risk in machine learning: Analyzing the connection to overfitting
S. Yeom, I. Giacomelli, M. Fredrikson, and S. Jha · 2018
Cited alongside, same era.
Hmrc forced to delete five million voice files, 2019
BBC · 2019
Cited alongside, same era.
Sirenattack: Generating adversarial audio for end-to-end acoustic systems
T. Du, S. Ji, J. Li, Q. Gu, T. Wang, and R. Beyah · 2019
Cited alongside, same era.
Logan: Membership inference attacks against generative models
J. Hayes, L. Melis, G. Danezis, and E. De Cristofaro · 2019
Cited alongside, same era.
P. Sarah · 2019
Closest in time.
Can you trust this prediction? Auditing pointwise reliability after learning
P. Schulam and S. Saria · 2019
Closest in time.
Auditing data provenance in text-generation models
C. Song and V. Shmatikov · 2019
Closest in time.
Privacy risks of securing machine learning models against adversarial examples
L. Song, R. Shokri, and P. Mittal · 2019
Closest in time.
Privacy-preserving adversarial representation learning in ASR: Reality or illusion?
B. M. L. Srivastava, A. Bellet, M. Tommasi, and E. Vincent · 2019
Closest in time.
Exploiting sound masking for audio privacy in smartphones
Y.-C. Tung and K. G. Shin · 2019
Closest in time.
Dangerous skills: Understanding and mitigating security risks of voice-controlled third-party functions on virtual personal assistant systems
N. Zhang, X. Mi, X. Feng, X. Wang, Y. Tian, and F. Qian · 2019
Closest in time.
California consumer privacy act (ccpa) website policy, 2020
D. U. CCPA · 2020
Closest in time.
Devil’s whisper: A general approach for physical adversarial attacks against commercial black-box speech recognition devices
Y. Chen, X. Yuan, J. Zhang, Y. Zhao, S. Zhang, K. Chen, and X. Wang · 2020
Closest in time.
Modelling and quantifying membership information leakage in machine learning
F. Farokhi and M. A. Kaafar · 2020
Closest in time.
Read between the lines: An empirical measurement of sensitive applications of voice personal assistant systems
F. H. Shezan, H. Hu, J. Wang, G. Wang, and Y. Tian · 2020
Closest in time.