Optimising the T9 Keyboard(torvaney.github.io) |
Optimising the T9 Keyboard(torvaney.github.io) |
I take a lot of walks. I often listen to an audio book while I do so. I also frequently get texts, or hear something in the book I may want to write down.
I've always wanted to be able to handle these situations without taking out my phone, but rather clicking buttons in my pockets. For example, I get a text while listening to music. Click a button to hear it read out. Type out my response. Send it. Open notes, write a reminder to buy bread. One-handed, blindly, while on a walk outside. No voice recognition - no "OK Google" nonsense.
I've considered getting a Twiddler - but they're expensive, and I don't think I can easily configure it to do these things - particularly browsing and responding to messages - or opening up a new note, while listening to an audiobook.
Does anyone know some combination of software and hardware that can do some of this for me?
I also like to take walks, sometimes listening to podcasts. The stock iOS voice recognition (the microphone button on the keyboard, not Siri) is quite good, I usually just talk into the phone without looking at the output. After the walk, I format and clean up the notes to fix any errors.
E.g. T9 puts 4 letters on '9', presumably a good choice as 'X' and 'Y' are rare. Would it be worth it to shift the 'V' from '8' to '9'. '3' has 'D' and 'E' on the same key, maybe move the 'D' to '2' ?
the whole predictive word thing was something pretty much alien to me.
My 2nd biggest problem with T9word is you need to look at the screen to verify the word you wanted is the one guessed. The other t9 keyboards allowed the user to type without looking at the screen. (Such as under the desk during class )
As I understand it, one of the big advantages of T9 had over other, more sophisticated forms of predictive text is that each word in the dictionary can be encoded in close to 1 byte. Given the constraints faced at the time, T9 feels to me like it is close to a local optimimum.
(Note: I have never used the computer with punch cards)
I dislike touch screen and voice controls, so using physical keys is better (whether T9 or other methods).
For this task, I was primarily interested in whether the task would work at all. My assumption is that given we can optimise for these texts, we could optimise for more representative datasets, too. Perhaps you think this is a weak assumption?
Do you think testing on a sample of totally different texts from different authors would be more convincing?