The IBM 2016 English Conversational Telephone Speech Recognition System
Table 5: Word error rates for individual acoustic models and frame-level score fusions on Hub5 2000.
['Model', 'WER SWB', 'WER CH']
All decodings are done with a 30K word vocabulary and a small 4-gram language model with 4M n-grams. We note that RNNs and VGG nets exhibit similar performance and have a strong complementarity which improves the WER by 0.6% and 0.9% on SWB and CH, respectively.