Machine learning challenge: learn english pronunciation

Question

Say you want to take CMU's phonetic data set input that looks like this:

ABERRATION  AE2 B ER0 EY1 SH AH0 N
ABERRATIONAL  AE2 B ER0 EY1 SH AH0 N AH0 L
ABERRATIONS  AE2 B ER0 EY1 SH AH0 N Z
ABERT  AE1 B ER0 T
ABET  AH0 B EH1 T
ABETTED  AH0 B EH1 T IH0 D
ABETTING  AH0 B EH1 T IH0 NG
ABEX  EY1 B EH0 K S
ABEYANCE  AH0 B EY1 AH0 N S

(The word is to the left, to the right are a series of phonemes, key here)

And you want to use it as training data for a machine learning system that would take new words and guess how they would be pronounced in English.

It's not so obvious to me at least because there isn't a fixed token size of letters which could possible map to a phoneme. I have a feeling that something to do with a markov chain might be the right way to go.

How would you do this?

Answer 1

Not entirely my field, but maybe build a neural network with several layers - earlier layers to guess the splitting of the words into sequential syllables, the later layers to guess the pronounciation of the said syllables.

Setting up a ANFIS-learning neural network is fairly straightforward for numerical data, for literal/phonetic data the task is undoubtedly several orders more complex.

Answer 2

The problem is called Grapheme-to-phoneme conversion, a subproblem of Natural Language Processing. Google brings up a few papers.