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More specifically: Gboard > Voice Typing > Faster voice typing It says its an 85MB download for US-English
Looks like this is on Android. Gboard iOS app doesn't have this setting.
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I refuse to put an amazon/apple/google surveillance device in my home, so I am very interested in a DIY digital assistant device. I'm aware of a few options but it seems like offline voice recognition is always a little sub-par. I am really looking forward to the day when an offline, open source digital assistant can compare in quality to a proprietary/cloud device.
> I refuse to put an amazon/apple/google surveillance device in my home... Do you have a smartphone? Because that's most likely an Apple or Google surveillance device.
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Ok, so what are the advantages of RNN over CNN based models for speech to text, with respect to any one of those factors you mentioned?
Well for example, some comparisons to the CNN paper you pointed to: - No comparison is given of number of model parameters. If optimizing strictly for model size, RNNs tend to be nice and compact. - The computational advantage of the CNN at training time is throughput. The advantage of RNN at decoding time is streaming latency. Running the CNN frame by frame as they are received removes the ability to run frames in p…
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There is also the transformer approach (eventually with local attention to bound latency), (like I'm doing in my project (Work in Progress) : https://github.com/GistNoesis/Wisteria/blob/master/SpeechToT... ), though it's in the same line of thought as the convolutional CTC. The RNN-T is a nice idea though, if I understand it correctly it's another approach to the alignment problem. In CTC, you are generating sequence…
We explored self-attention + CTC in our ICASSP 2019 paper ( https://arxiv.org/abs/1901.10055 ). Our implementation uses internal infra, so we're a ways from releasing code :( Hoping paper details suffice and help with the parameter search, and happy to respond to questions over e-mail. Would love to see an open-source implementation with local or directed attention built out!
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Gboard is governed by Google's catch all privacy policy, that allows them to gather all data and mine everything. If you have an android device with Google services and a firewall, you'll see that the device is constantly phoning home, which is also noted in the privacy policy. This does nothing for privacy, rather than provide the illusion of privacy.
Gboard uses differential privacy.
I'm mistrustful of Google's privacy stance, since they have a history of changing their privacy policy, then misleading users about it. Remember when they implemented personally-identifiable web tracking and sold it to users as "new features for your Google account"? Merging Doubleclick's tracking data with my Google account doesn't seem like a feature to me.
[0]: https://venturebeat.com/2017/04/06/following-apple-google-te...
[1]: https://www.propublica.org/article/google-has-quietly-droppe...
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I'm not trolling. The statement that RNN are somehow more noise-tolerant than CNNs does not make sense to me, and is not based on any literature about noise tolerance in NNs that I'm familiar with. Also, no arguments have been provided as to why this could possibly be the case.
Hi p1esk, RNNs can be more tolerant to noise because they can learn transient or dynamic attractors. If the inputs move an RNN into an attractor, small changes due to noise make little difference to the state. Recurrence can help with robustness in some other very important ways as well. Citations for this dates from the 80s and 90s. I don't know the best reference offhand. You could look at some old Hinton stuff if…
Nothing like this has been published AFAIK.
After you have the results of this experiment you can try to explain them with attractors and what not, but I would be surprised if there was much difference. Would make a good paper though!
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The offline aspect is hardly the main draw here though. As mentioned earlier in the article, the latency reduction is huge. Another aspect they didn't really cover is privacy implications. Lastly, you may not be offline, but dodgy connections can also be a pain if you need a stable stream of packets going back and forth.
I refuse to put an amazon/apple/google surveillance device in my home, so I am very interested in a DIY digital assistant device. I'm aware of a few options but it seems like offline voice recognition is always a little sub-par. I am really looking forward to the day when an offline, open source digital assistant can compare in quality to a proprietary/cloud device.
The thought that every interaction with my phone is being streamed in realtime to a third party server freaks me out.
Kudos to Google for working on this.
"But it’s sort of funny considering hardly any of Google’s other products work offline. Are you going to dictate into a shared document while you’re offline? Write an email? Ask for a conversion between liters and cups? You’re going to need a connection for that!" While offline, you might write email drafts, your blog, or even a book: https://medium.com/@augustbirch/what-i-learned-writing-an-en... What's missing is t…
It means google doesn't need to pay for all the servers busy doing speech recognition. They shifted that work to the user's device.