Unsupervised and Efficient Vocabulary Expansion forRecurrent Neural Network Language Models in ASR
Table 2: The perplexity and WER results on dev and test sets of TED-LIUM corpus
['LM', 'Perplexity Dev', 'Perplexity Test', 'WER (%) Rescore', 'WER (%) Dev', 'WER (%) Test']
Results: We evaluated LMs on perplexity and WER measure. The perplexity was computed on the reference data and the OOS words for vocabulary expansion were extracted from the reference data as well. The perplexity results computed on the test set show that VE-LSTM significantly outperforms KN5 and LSTM models by 31% and 18% relative, respectively.