아이들은 아직 xbox controller 가 무엇인지 알지 못한다.

나의 책상에는 여러 집기 들이 있지만 xbox controller 는 아이의 눈으로도 가지고 노는 물건으로 보인 것에 틀림 없다.

그렇게 책상에 올려두었다는 이유로 나의 xbox controller 는 어디론가 사라져버렸다.

안녕, Dead cells !

ref: http://cdn.intechopen.com/pdfs/15948/InTech-Phoneme_recognition_on_the_timit_database.pdf

Timit[1] data set originally contains 61 phones but in Graves RNN-T paper [2] and in many other pieces of literature use 39 phoneme sets.

Here is a mapping table from 61 classes to 39 classes, as proposed by Lee and Hon[3].

You can find the table far below. I'll fix it later..;

[1] URL: https://catalog.ldc.upenn.edu/LDC93S1

[2] A. Graves, Sequence Transduction with Recurrent Neural Networks, 2012

[3] Lee, K. and Hon, H. Speaker-independent phone recognition using hidden markov models. IEEE Transactions on Acoustics, Speech, and Signal Processing, 1989.

1. Decoding module

2. input noise, then can fully reproduce the RNN-T paper.

3. peephole, then can fully reproduce the CTC paper.

Timit data preperation..

prep timit dataset accoring to the RNN-T paper..

- train 3512

- valid 184 (drawn from training set) why...?

- test 192

but .. Timit composes of 4620 training, 1680 test uttrances..

git: https://github.com/sephiroce/kmlm, commit id: 1578f99

To input variable-length sequences into CuDNNLSTM layers, I needed to build-up a lambda function.

The return value of the lambda function was a logprob which is a scalar.

I faced "Can't+concatenate+scalars+(use+tf.stack+instead)" ...

The solution was to expand the value using tf.expand and I modified to use y_pred[0] not y_pred.

in lambda function.

import keras.backend as K

loss = tf.reduce_sum(full_logprob * seq_mask)

return K.expand_dims(loss, axis=0)

when compiling the models.

model.compile(loss={Constants.KEY_CCE:lambda y_true, y_pred: y_pred[0]},

optimizer=optimizer)

the problem seems to be solved.