The paper is co-authored by Jianyuan Zhong, @santty128, Pawel Swietojanski, Joao Monteiro, Jan Trmal, Yoshua Bengio. The work was started at JSALT 2019 (organized by @jhuclsp)
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PASE+ is an improved version of PASE for robust speech recognition. We employ an online speech distortion module, a revised encoder that better learns short- and long-term speech dynamics, and a refined the set of workers that encourage better cooperation.
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I'm a big fan of self-supervised deep learning. I think it will be widely adopted in the future in many fields, including speech. Maybe this will contribute to "make pre-training great again"! hahaha
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Awesome - we have quite a few papers from our team as well. See you at
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Thank you Vivek! See you in Barcellona
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Can we use it for cochlear implant processing? I am working on it
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I'm not familiar with this task. The idea is to learn a general speech representation that is useful for many applications, but I don't know if cochlear implant processing could be one of these tasks.
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Nice, more minions are added!! I am curious if self-sup. lear. can be used as a method for robustifying the encoder against adversarial attacks? The input can be distorted by adv pert. by fooling one of the workers. Can the other workers correct the infected one?
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Great work, can’t wait to test it on my projects! And thanks again for sharing the code with the community
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