The IBM 2016 English Conversational Telephone Speech Recognition System
Table 6: Comparison of word error rates for different LMs.
['LM', 'WER SWB', 'WER CH']
The component LMs are linearly interpolated with weights chosen to optimize perplexity on a held-out set. This new n-gram LM was used together with our best acoustic model to decode and generate word lattices for LM rescoring experiments. The WER improved by 1.0% for SWB. Part of this improvement (0.1-0.2%) was due to also using a larger beam for decoding and a change in vocabulary tokenization. We built a model M LM on each corpus and interpolated the models, together with the 36M 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). Another was trained on a 560M word subset of the LM training data: in order to speed up training for this larger set, we employed a hierarchical NNLM approximation [ Compared with the n-gram LM baseline, LM rescoring yielded a total improvement of 1.0% on SWB (7.6% to 6.6%) and 1.5% on CH (13.7% to 12.2%).