ericbolo

Thank you for your response! With the current BiLSTM setup, what would be required is then a feature window that slides over the input and includes for each input all...

For online decoding with neural nets, Kaldi recommends constructing an i-vector that summarizes speaker properties, and training the neural net with audio features + i-vector. In the absence of past...

Ok, I now have a pretty good understanding of the diarization/speaker normalization issues, none of them insurmountable in my application. For now, I can focus on the decoding of the...

Thank you for the link. I gave it a quick read, my only worry is that in the paper the data is pre-segmented with GMM/HMM, and the training does not...

Elaborating: from the paper I understand they sub-sample the data to avoid overfitting, so we don't have access to all the outputs of the utterance, possibly hampering CTC loss.

I've stumbled on this paper, which proposes a kind of BLSTM that is compatible with online decoding: http://ieeexplore.ieee.org/document/7953176/ Thought it might be of interest

I'm still very much interested in online decoding, yes, but unfortunately my hands are full this month and the next. If anyone is interested to work on this with me...

Hi Eric, I'm still interested in online decoding but company priorities have caught up to me and I can't do it single-handedly . This said, if we can team up...

From @fmeze 's answer above, implementing online decoding requires (1) audio streaming capability: work required but not exploratory, probably some open source tools available, (2) overhauling the model to be...

Regarding the branch, I for one am a lot more comfortable with tf. And sorry but what do you mean by "python in the loop". And what would be the...