The IBM 2015 English Conversational Telephone Speech Recognition System
Table 5: Comparison of word error rates for different language models.
['LM', 'WER SWB', 'WER CH']
This new n-gram LM was used in combination with our best acoustic model to decode and generate word lattices for further LM rescoring experiments. The WER improved by 0.5% for SWB and 0.3% for CallHome. Part of this improvement (0.1-0.2%) was due to also using a larger beam for decoding. We built a model M LM on each corpus and interpolated the models, together with the 37M n-gram LM. We built two NNLMs for interpolation. One was trained on just the most relevant data: the 24M word corpus (Switchboard/Fisher/CallHome acoustic transcripts).