AuToBI Version 1.2

I hadn't really planned for this current improvement to AuToBI be a milestone release.

I'm about halfway through an effort to get test coverage up to 90-95% of lines and 100% of classes.  I promise it'll get there eventually.

But in the mean time, I was playing with an improvement to how attributes are associated to data points.  I knew this was a significant source of inefficiency, but didn't quite expect this much.

Here are memory usage graphs for training a Pitch Accent Detection model on the Boston University Radio News Corpus -- about 22k data points and 136 features.  The first one is on my MacBookPro Laptop with 4G RAM (and a lot of other nonsense running).

The max memory usage of Version 1.1 was 1914Mb, with this improvement it tops out at 1049Mb. An improvement of about 45%.  (You'll notice it also ends a little bit quicker too, but this is probably because of fewer or quicker garbage collection calls.)

I figured I'd check on a compute server too, one of the Speech Lab @ Queens College's Quad Core Intel Xeon Processor E5450 (3.0GHz,2X6ML2,1333) with 4Gb RAM.  

Similar results here.  Max memory usage of version 1.1 was 2343Mb and with the improvement 1392Mb. Improving by 40%. (And the speed improvement is here too.) I don't have a good explanation for why the linux version is taking more memory to run, but for now I'll assume it has something to do with the difference to the JVM.

There are some other bugfixes in this version, but this is the big reason to upgrade.

The version 1.2 is available from github
git clone git@github.com:AndrewRosenberg/AuToBI.git

English Pronunciation by G. Nolst Trenité

This is a repost of a poem posted on spelling.wordpress.com that's been going around facebook today.

It's an incredibly elegant set of examples about why grapheme-to-phoneme (letter-to-sound) conversion is so difficult in English.  (Maybe this should be a required regression test for any TTS frontend...)

Please enjoy.
English Pronunciation by G. Nolst Trenité  (after the break)