Phoneme Level Language Models for Sequence Based Low Resource ASR
Table 6: % WER for languages using different decoding strategies on LMs trained on the Bible text. We see that in-vocabulary decoding using PLMs does much better than WFST based decoding.
['Babel', 'WFST', 'PLM']
We finally compare the robustness of the two best decoding strategies on domain mismatched conditions. Here, we assume the Bible as one of the sources of text in any low resource language and use it to train our language model. For generating the lexicon for both PLMs and WFSTs, we run the words of the Bible through Epitran G2P library. For the WFST, we train multiple n-gram language models with various discounting and choose the one that performs the best for our in-domain validation data. We then use this language model along with an in-domain acoustic model and decode it using the two strategies using the in-domain target dictionary.