Using embedding-based loss functions for phonetics/speech recognition.

emb_from_ab_dist.py

TODO write doc

There is no silver bullet, you need phonetically annotated speech corpora (e.g. TIMIT or the Buckeye corpus).

Then you can also work on the phonemic annotations, for that you need to transform words into phonemes. I did a hack-job using the CMU phonemic dict.:

python timit_words_to_phonemes.py

You need to have the TIMIT corpus with a train.scp leading to *.xyz files having corresponding *.wrd files with word-level annotation (look at the constant at the start of timit_words_to_phonemes.py).

python mlf_to_text.py < ~/postdoc/datasets/TIMIT_train_dev_test/train/train.mlf >> timit_train_from_phones.txt

or

python mlf_to_text.py --forcealigned --timitfoldings < ~/postdoc/datasets/TIMIT_train_dev_test/aligned_train.mlf >> timit_train_from_phones.txt
python train_word2vec.py timit_train_from_phones.txt

Same for the Buckeye corpus.

Comparing two embeddings is as simple as:

python train_word2vec.py timit_train_from_phones.txt timit_train_from_words.txt

or

python train_word2vec.py timit_train_from_phones.txt buckeye_train_from_phones.txt

For the Buckeye corpus, "tq" (glotal stop in "cat") folded to "sil".

For the TIMIT corpus, "dx" (flap in "butter") inexistent in "words" (phonemic annotation) version.