On the Effectiveness of Neural Text Generation based Data Augmentation for Recognition of Morphologically Rich Speech
Table 2: WER of the online ASR system using the proposed language models
['[BOLD] Token type Word', '[BOLD] Model BNLM', '[BOLD] # of n-grams [million] 5.0', '[BOLD] Memory usage [GB] 1.3', '[BOLD] WER [%] 21.9', '[BOLD] WERR over Word/Morph BNLM [%]', '[BOLD] WERR over Word/Morph BNLM [%]']
First we discuss the online ASR results (see The n-gram model estimated on the corpus that was generated with the word-based RNNLM (RNN-BNLM 100M) has a slightly higher WER than the baseline word-based BNLM (2.6% relative WER increase), but with the interpolated model (BNLM + RNN-BNLM 100M) we are able to significantly outperform both of them (2.7% rel. WERR).