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marcoyang1998 2023-07-27 11:40:32 +08:00
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docs/source/decoding-with-langugage-models/index.rst
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@ -4,6 +4,26 @@ Decoding with language models
This section describes how to use external langugage models
during decoding to improve the WER of transducer models.
The following decoding methods with external langugage models are available:
.. list-table:: Description of different decoding methods with external LM
:widths: 25 50
:header-rows: 1
* - Decoding method
- Description
* - `modified_beam_search`
- This one does not use language model. Beam search (i.e. really n-best decoding, the "beam" is the value of n), similar to the original RNN-T paper
* - `modified_beam_search_lm_shallow_fusion`
- As `modified_beam_search` but interpolate RNN-T scores with language model scores, also known as shallow fusion
* - `modified_beam_search_LODR`
- Low-order Density ratio. As `modified_beam_search_lm_shallow_fusion`, but subtract score of a (BPE-symbol-level) bigram backoff language model used as an approximation to the internal language model of RNN-T.
* - `modified_beam_search_lm_rescore`
- As `modified_beam_search`, but rescore the n-best hypotheses with external language model (e.g. RNNLM) and re-rank them.
* - `modified_beam_search_lm_rescore_LODR`
- As `modified_beam_search_lm_rescore`, but also subtract the score of a (BPE-symbol-level) bigram backoff language model during re-ranking.
.. toctree::
:maxdepth: 2