ishine/lattice_rnn

Bi-directional Lattice Recurrent Neural Networks for Confidence Estimation

Bi-directional Lattice Recurrent Neural Networks for Confidence Estimation

This repository is the code used in our paper:

Bi-directional Lattice Recurrent Neural Networks for Confidence Estimation

Qiujia Li*, Preben Ness*, Anton Ragni, Mark Gales (* indicates equal contribution)

ICASSP 2019

Model

In short, this model is an extension of classical LSTM network that runs on linear sequences to BiCNRNN that runs on confusion networks (a.k.a. sausages) or BiLatRNN that runs on lattices.

This allows intermediate information produced by the speech recogniser to be fully utilised to predict not only confidence scores on one-best sequences, but on all alternative word hypotheses, which are useful in many confidence-related tasks.

The model gives significant improvement on confidence scores. Precision-recall curves are shown below.

one-best paths	lattices

For more details, please refer to the paper or the thesis.

Usage

Dependencies

• python 3.6.3
• pytorch 0.3.1
• numpy 1.14.0
• matplotlib 2.1.2
• scikit-learn 0.19.1
• tqdm 4.19.5

Commands

To train the model,

OMP_NUM_THREADS=1 python3 main.py

For detailed options,

python3 main.py --help

Note that the environment variable OMP_NUM_THREADS is essential for CPU parallelisation.

Data Pre-processing

This repository assumes a root directory with pre-processed data organised as follows:

root/
  |
  |-- data/
  |     |
  |     |-- lattices/
  |     |-- target/
  |     |   train.txt
  |     |   train_debug.txt (if in debug mode)
  |     |   cv.txt
  |     |   test.txt
  |     |   stats.npz
  |
  |-- exp/
  |     |-- (saved models)
  |     |-- ...

In the data/ directory:

• lattices/ contains the pre-processed data structures from one-best sequences, or confusion networks, or lattices, which are stored as zipped archive files by numpy with suffix .npz. Each one has the following attributes:

  • topo_order - a list of node indices that follows a topological order;
  • child_2_parent - a dictionary that maps from a node index to a dictionary, whose key is the index of the parent node and the value is the index of the connecting edge for lattices or a list indices of the connecting edges for confusion networks. This is used for the forward recursion;
  • parent_2_child – a dictionary that maps from a node index to a dictionary, whose key is the index of the child node and the value is the index of the connecting edge for lattices or a list indices of the connecting edges for confusion networks. This is used for the backward recursion;
  • edge_data – a numpy 2d array (matrix) containing all relevant information from the source file where the row index is the edge index. For an arc in a lattice, the information could include the word, the start time and the end time, LM and AM scores. For an arc in a confusion network, the arc posterior probability, the start and the end time should be available;
  • ignore – a list of edge indices whose corresponding word is one of the following <s>, </s>, !NULL, <hes>, which are due to be skipped during training of the network.

• target/ contains the pre-processed training targets which correspond to the ones in lattices/. They are also stored in .npz format. Each one has the following attributes:

  • target - a list of target confidence scores for each arc in the corresponding lattice, with each element being either 0(incorrect) or 1(correct);
  • indices - a list of arc indices on one-best sequences in the structure;
  • ref - a list of target confidence scores on one-best sequences.

• *.txt stores the absolute paths of data where each line corresponds to one sample in lattices/ directory.

• stats.npz stores the statistics of the input feature, which is used for data normalisation upon loading. It has the following attributes:

  • mean - the mean of input feature vectors across the dataset;
  • std - the standard deviation of input feature vectors across the dataset.

References

@inproceedings{Li2019BiLatRNN,
  title={Bi-directional Lattice Recurrent Neural Networks for Confidence Estimation},
  author={Li, Qiujia and Ness, Preben M. and Ragni, Anton and Gales, Mark J. F.},
  booktitle={ICASSP},
  year={2019},
  address={Brighton}
}

@inproceedings{Ragni2018Confidence,
  title={Confidence Estimation and Deletion Prediction Using Bidirectional Recurrent Neural Networks},
  author={Ragni, Anton and Li, Qiujia and Gales, Mark J. F. and Wang, Yu},
  booktitle={SLT},
  year={2018},
  address={Athens}
}