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:

Table 1 LM-rescoring-based methods vs shallow-fusion-based methods (The numbers in each field is WER on test-clean, WER on test-other and decoding time on test-clean)

Decoding method	beam=4
modified_beam_search	Beam search (i.e. really n-best decoding, the “beam” is the value of n), similar to the original RNN-T paper. Note, this method does not use language model.
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	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.

• Shallow fusion for Transducer
• LODR for RNN Transducer
• LM rescoring for Transducer