Personal VAD by Google

August 16, 2019August 16, 2019 by admin

https://arxiv.org/abs/1908.04284

Personal VAD: Speaker-Conditioned Voice Activity Detection

Shaojin Ding, Quan Wang, Shuo-yiin Chang, Li Wan, Ignacio Lopez Moreno

Comments: To be submitted to ICASSP 2020Subjects: Audio and Speech Processing (eess.AS); Machine Learning (cs.LG); Machine Learning (stat.ML)

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Thang Luong @lmthang·

12 Nov

Another view of Noisy Student: semi-supervised learning is great even when labeled data is plentiful! 130M unlabeled images yields 1% gain over previous ImageNet SOTA that uses 3.5B weakly labeled examples! joint work /w @QizheXie, Ed Hovy, @quocleix https://t.co/VUmtRlK8xk https://t.co/gdqmbCCebz

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Gary Marcus @GaryMarcus·

12 Nov

My first attempt at conversation w GPT-2. It evades questions, flunks basic arithmetic, and gets caught up in probabilities of blank spaces.

Conversations with bots like Eliza and GPT-2 can look decent for short periods, but fail if you insist they keep track of anything.

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Quoc Le @quocleix·

12 Nov

Method is also super simple:

1) Train a classifier on ImageNet
2) Infer labels on a much larger unlabeled dataset
3) Train a larger classifier on the combined set
4) Iterate the process, adding noise

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Quoc Le @quocleix·

12 Nov

Want to improve accuracy and robustness of your model? Use unlabeled data!

Our new work uses self-training on unlabeled data to achieve 87.4% top-1 on ImageNet, 1% better than SOTA. Huge gains are seen on harder benchmarks (ImageNet-A, C and P).

Link: https://t.co/ZYDaef6sdp

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Allyson Ettinger @AllysonEttinger·

12 Nov

Further evidence for what I found ("What BERT is not" https://t.co/9P62YDLD1F): these models don't handle negation, likely because LM pre-training isn't cut out for negation. (And they even continue the bird theme.) https://t.co/6kH8wQdsWG

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