An investigation into language model data augmentation for low-resourced STT and KWS
Résumé
This paper reports on investigations using two techniques for language model text data
augmentation for low-resourced automatic speech recognition and keyword search. Low-
resourced languages are characterized by limited training materials, which typically results
in high out-of-vocabulary (OOV) rates and poor language model estimates. One technique
makes use of recurrent neural networks (RNNs) using word or subword units. Word-based
RNNs keep the same system vocabulary, so they cannot reduce the OOV, whereas subword
units can reduce the OOV but generate many false combinations. A complementary
technique is based on automatic machine translation, which requires parallel texts and is
able to add words to the vocabulary. These methods were assessed on 10 languages in the
context of the Babel program and NIST OpenKWS evaluation. Although improvements vary across languages with both methods, small gains were generally observed in terms of word error rate reduction and improved keyword search performance.