Japanese Tranliteration to English

Japanese 'katakana' are special symbols used to create japanese pronunciations for foreign pronouns and technical terms. A problem of interest is to recover the original english word given the japanese pronunciation associated with a set of katakana.

One pipeline for this process is a noise channel model that assumes a given katakana instance was produced by the following process:

• An english word E was generated
• English word E was distorted into an english pronunciation EP via a noisy channel.
• English pronunciation EP was distorted to japanese pronunciation JP via a noisy channel.

We store the statistics for each of the processes in wfsa/wfsts.:

• The probability of generating an english word can be stored in a wfsa.
• The probability of generating an english phenome sequence given a word can be stored in a wfst.
• The probability of generating a japanese phenome sequence given an english phenome sequence can be stored in a wfst.

We can learn the statistics of english word distributions from appropriate large bodies of text. This is stored in eword.wfsa. We can use some naive scheme for generating english phenomes from english characters, where each transition from english character to phenome-sequence possibility is given equal probability. This is stored in eword-epron.wfst.

Lastly, we are interested in learning alignment statistics for english pronunciations to japanese pronunciations and storing these in epron-jpron.wfst. We have a set of unaligned pronunciation pairs in epron-jpron-unsupervised.data, and will learn the alignments statistics via an EM algorithm.

Once we have these statistics stored in WFSA/WFSTs, we use the carmel sofrware package (http://www.isi.edu/licensed-sw/carmel/) to execute the Viterbi algorithm and to output the most likely english source word that generated the japanese pronunciation.

Running

To learn epron-jpron-unsupervised.wfst, run python run.py.

This reads in epron-jpron-unsupervised.data and:

• Runs an EM algorithm to compute the probabilities for the noisy channel model (the probability of seeing a japanese phenome sequence given one english phenome)
• Produces carmel-formatted epron-jrpon-unsupervised.wfst from this learned model
• Calculates the probabilities of each possible alignment, given the learned channel model, for the first five phenome pairings in epron-jpron-unsupervised.data
• Produces epron-jpron.alignment
• Calculates the accuracy of produced epron-jpron.alignment with respect to epron-jpron.data (accurate alignment data)

Translation with Carmel Once epron-jpron-unsupervised.wfst has been learned, run:

Expected Output:

TODO

• Add carmel commands to verify translation process.
• Clean up, modularize, document EM and parallel_data.
• Seperate out data and src files.
• Move into jupyter notebook with visualizations.
• Build hooks into carmel or write own viterbi code (slower in python...).
• Build web interface.