Fingerspelling is just a path for borrowing individual words from English. It is not a part of native ASL vocabulary or grammar; that is to say, ASL does not consist of fingerspelled English sentences.
I looked into this a bit for classic Macs (8MHz 68000), and found that they didn't have the computing power to complete a handshake before the timeout was up. I could be wrong though, and maybe some faster implementation is possible...
It makes sense. Timing is important in crypto as you don't want Eve to have enough time to brute force the secret out of the exchange between Alice and Bob.
We'll need to build new network cards with hardware accelerators.
And be back to the stage where the most powerful computer in the office was a peripheral (back then we had a printer with more memory than all our Macs combined and a flashy AMD 29K CPU)
SEEKING WORK | USA, Eastern timezone | remote only | part-time
Technologies: Python, NumPy, Torch, C++, Linux
I specialize in speech and audio processing, but I'm open to a wide variety of back-end work. I like short-term or intermittent jobs, but would also consider longer-term.
I started as a developer in 2005, bringing to production and then maintaining and documenting a distributed entity resolution system in C++ and Python. I started working with speech and audio in 2013, primarily working solo on my own Web application for foreign language pronunciation learning, which you can try at https://accentlab.net . This involved developing the full stack, from collecting and cleaning speech data, to training my own custom acoustic models for pronunciation analysis in Python with NumPy and Torch, to learning Flask, Django, Bootstrap and JavaScript for the web interface, to user testing.
For freelancing, I prefer jobs involving speech or audio processing, but I'm open to a wide variety of tasks, primarily backend. Jobs I've particularly enjoyed include: a quick bounty for rooting out synchronization bugs in a large multithreaded C++ codebase; developing custom audio processing modules with SciPy's filter library; creating a speech keyword-spotting module for a telephone assistant using a pre-trained Kaldi model; training a custom classifier for music synthesizer samples.
Other jobs I've done include using APIs for HubSpot, Zoho, AdSense, etc.; helping businesses with bulk generation of PDFs; developing a cross-platform TkInter application.
My Upwork profile https://www.upwork.com/freelancers/~01a1b1fe58b78de9cc is currently only visible to Upwork users due to lack of recent activity; my ratings are all 5-star and I can provide you with the reviews if you're interested.
Please get in touch via email, craig dot jb at gmail with some information about your project. Looking forward to hearing from you!
All the lentils from the supermarket that I've tried (and all legumes for that matter) have successfully sprouted: brown, pardina, black (beluga), and red. They're also good raw when sprouted, but not pardina, which I found ended up with occasional super-hard ones that hurt my teeth.
Both keyboards do exist. The Cherokee layout is included in OSX and iOS, while Navajo is available for download. You can find pictures of Cherokee keycaps in use, and I think there were also typewriters and certainly printed type in Cherokee.
Try reading a good text multiple times, even to the point of memorizing it. Especially in the past, many cultures viewed memorization as essential to truly engaging with a text. Another way to engage more deeply with a good, classic text is to read commentary on it.
Obviously you can't treat everything you read this way, but for a few really good classics this has been a fruitful approach for me. For a novel, maybe you could just memorize a few passages. When you have the words rolling around in your head, you'll find that you recall them at just the right time.
Flour often contains eggs from insects like flour weevils, and if you let flour sit in the cupboard too long they'll hatch. But I would be disgusted to find 50 whole adult weevils per 5 lb. bag, on average. Maybe they're counting eggs?
Is this really speech recognition from raw waveforms? It looks like they're extracting MFCC features from the raw audio, and using that as input to the neural network. I thought that the point of WaveNet was that it took the raw waveform directly as input, unlike previous architectures which first extract spectral features such as MFCCs to use as the input.
Apparently, they tried to use the raw audio waveform with the original setup from the WaveNet paper but couldn't get it to train on their TitanX, so they used MFCCs instead. It's not exactly clear why this is the case.
"Second, the Paper added a mean-pooling layer after the dilated convolution layer for down-sampling. We extracted MFCC from wav files and removed the final mean-pooling layer because the original setting was impossible to run on our TitanX GPU." [1]
