Phoneme Level Language Models for Sequence Based Low Resource ASR
Table 3: % WER for each of the languages used using different kinds of decoding strategies; WFST decoding using word language models, open-vocabulary decoding using CLMs and in-vocabulary decoding using PLMs. Almost always, PLM based decoding performs better than CLMs and as good as WFST.
['Babel', 'WFST', 'CLM', 'PLM']
However, it is often impossible to collect and clean such large amounts of data in low-resource languages, and these open vocabulary decoding approaches do not perform well. We think this is because the language models that are trained might not reliably output valid OOV words. We see that PLM based decoding does much better than open vocabulary CLM decoding. These improvements stem either from the ability to build higher quality acoustic models using phonemes instead of characters, or in-vocabulary decoding, both of which our proposed model facilitates over prior work. We also observe that this approach performs comparatively with WFST based decoding.