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Automatic speech recognition (ASR) systems based on deep neural networks are weak against adversarial perturbations.
“Connectionist temporal classification: labelling unsegmented sequence data with recurrent neural networks,”
Alex Graves, Santiago Fernández, Faustino J. Gomez, and Jürgen Schmidhuber, · 2006
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“Sinkhorn distances: Lightspeed computation of optimal transport,”
Marco Cuturi, · 2013
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“Librispeech: An ASR corpus based on public domain audio books,”
Vassil Panayotov, Guoguo Chen, Daniel Povey, and Sanjeev Khudanpur, · 2015
Earlier work this paper cites.
“A survey of deep learning methods and software tools for image classification and object detection,”
Pavel Nikolaevich Druzhkov and Valentina Dmitrievna Kustikova, · 2016
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“Listen, attend and spell: A neural network for large vocabulary conversational speech recognition,”
William Chan, Navdeep Jaitly, Quoc V. Le, and Oriol Vinyals, · 2016
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“A survey on deep learning in medical image analysis,”
Geert Litjens, Thijs Kooi, Babak Ehteshami Bejnordi, Arnaud Arindra Adiyoso Setio, Francesco Ciompi, Mohsen Ghafoorian, Jeroen Awm Van Der Laak, Bram Van Ginneken, and Clara I Sánchez, · 2017
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“A survey of deep learning techniques in speech recognition,”
Akshi Kumar, Sukriti Verma, and Himanshu Mangla, · 2018
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“Deep learning for sentiment analysis: A survey,”
Lei Zhang, Shuai Wang, and Bing Liu, · 2018
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“Training augmentation with adversarial examples for robust speech recognition,”
Sining Sun, Ching-Feng Yeh, Mari Ostendorf, Mei-Yuh Hwang, and Lei Xie, · 2018
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“wav2vec: Unsupervised pre-training for speech recognition,”
Steffen Schneider, Alexei Baevski, Ronan Collobert, and Michael Auli, · 2019
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“Detecting adversarial attacks on audio-visual speech recognition,”
Pingchuan Ma, Stavros Petridis, and Maja Pantic, · 2019
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“Characterizing audio adversarial examples using temporal dependency,”
Zhuolin Yang, Bo Li, Pin-Yu Chen, and Dawn Song, · 2019
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“Super-convergence: Very fast training of neural networks using large learning rates,”
Leslie N Smith and Nicholay Topin, · 2019
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“A survey on theories and applications for self-driving cars based on deep learning methods,”
Jianjun Ni, Yinan Chen, Yan Chen, Jinxiu Zhu, Deena Ali, and Weidong Cao, · 2020
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“Characterizing speech adversarial examples using self-attention u-net enhancement,”
Chao-Han Huck Yang, Jun Qi, Pin-Yu Chen, Xiaoli Ma, and Chin-Hui Lee, · 2020
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“Detecting audio attacks on ASR systems with dropout uncertainty,”
Tejas Jayashankar, Jonathan Le Roux, and Pierre Moulin, · 2020
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“MP3 compression to diminish adversarial noise in end-to-end speech recognition,”
Iustina Andronic, Ludwig Kürzinger, Edgar Ricardo Chavez Rosas, Gerhard Rigoll, and Bernhard U. Seeber, · 2020
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Haichao Zhang and Jianyu Wang, · 2019
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“Decoupled weight decay regularization,”
Ilya Loshchilov and Frank Hutter, · 2019
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“End-to-end face parsing via interlinked convolutional neural networks,”
Zi Yin, Valentin Yiu, Xiaolin Hu, and Liang Tang, · 2021
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“Adversarial attack and defense strategies for deep speaker recognition systems,”
Arindam Jati, Chin-Cheng Hsu, Monisankha Pal, Raghuveer Peri, Wael AbdAlmageed, and Shrikanth Narayanan, · 2021
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