NN-grams: Unifying neural network and n-gram language models for speech recognition
Table 3: Impact of NN-grams components on WER.
['NN-grams components', 'VS', 'DTN']
NN-grams consist of two components: word embeddings and the n-gram counts. We expect this result considering that using n-gram counts typically improves the performance for short sentences, which is the case for both VS and DTN (Average number of words/sentence on VS and DTN is 3.7 and 6.5 respectively). For DTN, word embeddings contributed to an additional improvement in WER, that can be attributed to the longer sentence length in DTN.