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Commit 0287be93 authored by SHEIKH Imran's avatar SHEIKH Imran
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details on Python pre-requisites

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......@@ -26,7 +26,9 @@ Readers interested in the high level design and experimental evaluation of these
- (more) un-transcribed speech data.
- a dev set containing application specific transcribed speech data
- Err2Unk based training requires:
- the [Keras](https://keras.io/) Python library to train neural network models for STT error detection.
- Python 2.7 (Python 3.X support is on ToDo list but should be straightforward for users/developers/contributors.)
- Python sklearn and matplotlib libraries
- the [Keras](https://keras.io/) Python library (verion 2.3.1) to train neural network models for STT error detection.
- the [kenlm](https://github.com/kpu/kenlm) Python module to extract language model related features for error detector.
- Dialogue state based training requires:
- the [SRILM](http://www.speech.sri.com/projects/srilm/) tool. If you have installed Kaldi, you can install the SRILM tool with the *tools/extras/install\_srilm.sh* script in your Kaldi installation.
......@@ -95,7 +97,7 @@ Err2Unk based semi-supervised training of STT models will typically go through t
>`python local/err2unk/errdet/train_3c_error_tagger_on_dev.py dev_saus_feats_n_labs err_model_dir`
'err_model_dir' stores the resulting error tagger model
'err_model_dir' must be created by user and will store the resulting error tagger model
Note that feed forward neural network based error detector can also be tried with the python script *local/err2unk/errdet/train_3c_error_mlp_on_dev.py*.
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