Improvements to Deep Convolutional Neural Networks for LVCSR
Table 11: HF Seq. Training WER Per CE Iteration
['CE Iter', '# Times Annealed', 'CE WER', 'HF WER']
Finally, we explore if we can reduce the number of CE iterations before moving to sequence training. A main advantage of sequence training is that it is more closely linked to the speech recognition objective function compared to cross-entropy. Using this fact, we explore how many iterations of CE are actually necessary before moving to HF training. Note that HF training is started and lattices are dumped using the CE weight that is stopped at. Notice that just by annealing two times, we can achieve the same WER after HF training, compared to having the CE weights converge. This points to the fact that spending too much time in CE is unnecessary. Once the weights are in a relatively decent space, it is better to just jump to HF sequence training which is more closely matched to the speech objective function.