Domain Expansion in DNN-based Acoustic Models forRobust Speech Recognition
Table 1: WERs of different domain expansion methods on the original (Org) and the new domains (New). For each approach, among the different settings of regularization parameters, the one that results in the best overall performance for both original and new domains (i.e., the lowest average of WERs) is reported. The relative WER increase of the methods compared to multi-condition (MC) training is also reported (Rel-MC)
['[EMPTY]', 'Method', 'Org', 'New', 'Avg', 'Rel-MC']
We conduct several experiments to evaluate the performance of the domain expansion methods explored in this study. As mention in previous sections, each domain expansion technique has a controlling hyper-parameter that provides a compromise between keeping the model performance for the original data and learning the new domains. In the first set of experiments, we tune these hyper-parameters to achieve the best overall performance for both the original and the new domains. The performance of the multi-condition system can be considered as an upper bound for the performance of domain-expansion methods. Forgetting effect. The original model performs well for the Libri clean test set that matches the training set conditions; however, for the unseen domains, the performance of the model degrades significantly. Fine-tuning (FT) We also show how the SKLD approach performs in the same setting. SKLD can successfully preserve the model performance for the original data while learning the new data. The overall performance of the model on both old and new domains demonstrates that SKLD performs significantly better than naive fine-tuning for domain expansion. The performance of WCA and EWC. WCA as a naive domain expansion method performs significantly better than fine-tuning in finding a compromise between the performance of the original and new domains. For EWC experiments, since the diagonal of the Fisher information matrix F is zero for many of the original model’s weights, simply applying EWC does not preserve the model performance. We found that adding an empirically determined value of 1 to the elements of the matrix addresses the problem. The performance of SKLD and Hybrid SKLD-EWC. For HIS accent, SKLD is still slightly better than EWC. The hybrid SKLD-EWC that benefits from both SKLD and EWC results in the best overall performance.