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NLP class project to determine word pronunciations given a training model.
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README.md

Phonemer: Grapheme-to-Phoneme classifier

This application was a Natural Language Processing class project at RIT to determine word pronunciations. It also examines the abilities of Neural Networks as classifiers of Phonemes.

Please note that we did not include a data set in this repo, but a previously trained Neural Net instead. Use this .nn file for testing purposes. If you would like train one yourself, you will need to look for a dataset with aligned graphemes and phonemes. We suggest looking at the PMTools paper by Richard Sproat (2001).

How to Use Phonemer

For finding the pronunciation of a word?

A NeuralNet file is needed to know how to pronounce a word correctly, we have trained one for you and its located in the 'nets' directory. To load the nn and check the pronunciation of a word, run the following:

cd src
./phonemer.py -t ../nets/saved.nn -w <YOUR WORD HERE>

By replacing '' with a random word, you will get a print out of the pronunciation, character by character.

For generating a new neural net file?

A neural netfile is just a pickled representation of our NeuralNet class. To generate one you would need an aligned dataset that we can generate enough features to train the network. Please see our PhonemeDataFile class for more information on how we parse a dataset. Otherwise you can substitute your own parsing class.

The neural net can be customized and tweaked in order to tune on a specific dataset or language. Some options for tuning are as follows:

  • -n = Change the number of nodes per hidden layer. We've found that around 100 is best. Must be > 0, default is 100

  • -l = Change the number of hidden layers. Typically this isn't as useful as the previous option but we provided it anyway. Must be > 0, default is 1.

  • -p = Run PCA algorithm over the feature vectors before classification. Must be > 0, default is disabled.

An example command for generating nn file:

cd src
./phonemer.py -d ../data/dataset.dat -n 150 -l 1 -f ../nets/saved2.nn

This will save a neural net with 150 nodes on 1 hidden layer to ../nets/saved2.nn.

For splitting a large dataset up for testing?

Sometimes datasets are larger than you need them to be for development purposes. Phonemer can split your dataset up for you based on a percentage:

cd src
./phonemer.py -d ../data/large.dat -s 0.8

This will split large.dat dataset into two large_1.dat and large_2.dat in 80% and 20% chunks.

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