Large Margin Neural Language Model
Table 2: Published WERs on WSJ dev93 and eval92 set
['ASR Models', 'WER dev93', 'WER eval92']
Our ASR model has one convolution layer, followed by 5 bidirectional RNNs and one fully connected layer, with a CTC loss on top. The text labels of the training set are used to train a 4-gram language model, which is employed in the ASR decoder. The beam search decoder has a beam width of 2000. Before beam rescoring, this ASR system achieves a WER of 12.16 on dev93 set and 7.69 on eval92 set. To put this into perspective, we list some previous state-of-the-art system in Tab. Compared with them, our baseline is already very competitive.