Toward domain-invariant speech recognition via large scale training
Table 7: Results on the Telephony test set when adapting the multidomain model and the Voicesearch-Dictation model using in-domain data. Also shown are the corresponding baselines from Tab. 6, and when using a model trained only on Telephony training data.
['[BOLD] Test set', '[BOLD] Word error rate']
One of the most important advantages of multidomain modeling is that it allows for easy and quicker adaptation to new conditions. To demonstrate this, we use varying amounts of in-domain data to improve performance on the Telephony domain, which was not used to train the models in Sec. We experiment with 10 hours, 30 hours, 100 hours and 700 hours (approx.) of training data. For adaptation, we use the relatively simpler fine-tuning approach. All layers of the model are tuned. A lower learning rate and early stopping is used to prevent over-fitting. We also train a separate model using the 700 hours training set, with model parameters initialized from scratch. Results are shown in Tab.