Acoustic Model Optimization Based On Evolutionary Stochastic Gradient Descent with Anchors for Automatic Speech Recognition
Table 3: CE loss and WER of ESGD with anchors on SWB300.
['model', 'loss', 'WER SWB', 'WER CH']
The first two rows of the table provide the SGD and ESGD baselines, respectively, on SWB and CH test sets. When using ESGD baseline as the initial anchor, the CE loss improves from 1.551 to 1.536 after 20 generations. It slightly improves WER on CH (18.2% → 18.1%) but the WER on SWB stays the same (10.0% → 10.0%). If we start the ESGD all over with this best model, after another 40 generations with iterated anchors, the loss drops to 1.512 and the WER on CH further reduces to 17.7% and the WER on SWB reduces to 9.9%.