EvoSpeak - Progress and Ideas

I'm still working on EvoSpeak, getting the engine all set up. I finished the random generating algorithms that will provide training material from which EvoSpeak will "learn." They also define the basis of the new grammar system, whose syntax is simpler even than that of GrammGen, but whose power is much greater.

Next I need to create the functions that will analyze the training material to figure out what attributes they have in terms of melody and rhythm. All of this analysis data will be stored in a training file that will also indicate how well the user likes the material. After a certain number of training pieces have been graded by the user, EvoSpeak will dig up all the analysis data and perform an extensive statistical analysis on it to try find correlations and develop a "brain," so to speak, that will allow the program to function as an output device.

I'm still trying to figure out exactly what variables/attributes should be part of the "brain." This has always been my problem with statistical models; I've never known exactly what variables to draw statistics from. Now I've got to tackle the issue. I'll start simple - state variables (such as what beat the rhythmic or melodic object falls on) and first-order memory variables (what the last rhythmic or melodic object was) should work fine for the first version.

I plan to have EvoSpeak set up in an intuitive "leveling" kind of way that reflects a simple game. Before EvoSpeak will work, the user must first create a new "creature" that speaks a certain "language." At first the creature will have no idea how to speak the language; like a child, the creature must be shown how to use words to make sentences. The user "trains" the creature by listening to samples and rating them on a scale of 1 (strong dislike) to 5 (strong like). The creature gains XP (experience points) when the user listens to samples and submits ratings. When the creature has enough XP, it can "level up." During the leveling-up process (unbeknownst to the user), the creature actually goes back and analyzes all of the samples and ratings and essentially "learns" from the previous batch of material. The leveling system is good because it will ensure that correlations are relatively strong before they will be used to generate (i.e. the creature won't work without the user having trained it to a certain level).

At higher levels, creatures may learn the ability to analyze deeper variables other than states and first-order memories. Perhaps the creature gains more memory with each level (this is equivalent to increasing the order of the Markov chain analysis). Or perhaps the creature starts analyzing surface contours (3-variable functions) instead of 2-dimensional dependencies.

These are pretty abstract and crazy ideas, but I think they make sense, and I think they will provide a refreshing and intuitive break from the usual grind of KBSs. I'm interested to start training my first creature! And if the leveling system actually makes the music sound better (as intended)...well...I think I could spend all day leveling my creatures (is this starting to sound like Pokemon? That's neither my intent nor my inspiration).