2. Use script in the labels section of the Makefile script in the data folder to convert the .utt file into monophone labels and full-context labels. I will not write about this step in this post.
And here come the problem. The generated .utt files are from an installed voice of Festival and are almost certainly not aligned to your .wav files, so the full-context labels will be good for synthesis, but training.
with the format <end time> <color> <phone> ;. The color is not important here and can be any number; the end time is in seconds and the start time is derived automatically from the previous end time. This format can be converted very easily from HTK monophone label format. Here I assume that you should already have the monophone aligned labels, so it would not be a problem preparing the xLabel files.
And now you have the .utt files that contain all necessary linguistic information as well as aligned exactly as you want. Just use them as in any HTS demo and you are done!
This would be interesting for me as well! Could you already find a solution for creating the full-context labels automatically?
Not yet. I am currently using NIST tool to find the syllable boundary, FESTIVAL to get the POS tags, and then my own scripts to combine those data and generate all the locational information.
I still don’t know how to automatically extract the stress and accent information, which seems to be quite important for generating good prosody.
Thanks for the tips. They solved some of my problems.
I was under the impression that making .utt using Festival does compute Syllable boundary, POS, stress and accent info. and etc. Aren’t these the contextual features?
Yes the labels created by Festival have all the necessary phonetic and linguistic contexts and can be used for synthesis. However, I believe those labels should not be used as training data because I might not match your training speech (e.g. the pronunciation chosen by festival might not match the pronunciation in the input wav files). Using EHMM as in my updated post will give you better labels to train a voice model.