The IBM 2016 English Conversational Telephone Speech Recognition System
Table 4: Word error rates on Hub5 2000 for DNN and LSTM models. All models are trained on 2000 hours with cross-entropy and sequence discriminative training.
['Model', 'WER SWB CE', 'WER SWB ST', 'WER CH CE', 'WER CH ST']
As can be seen, the LSTM model with carried over start states is much better at predicting the correct HMM state. We surmise that this is because the increased memory of the LSTM with carried over start states is in conflict with the state sequence constraints imposed by the HMM topology and the language model. Additionally, we show the WERs of the DNN used for the bottleneck features and of a 4-layer 512 unit LSTM. We observe that the 4 layer LSTM is significantly better than the DNN and the two single layer LSTMs trained on bottleneck features.