Toward domain-invariant speech recognition via large scale training
Table 5: Results using domain-specific models.
['[BOLD] Test set', '[BOLD] Sample  [BOLD] rate', '[BOLD] Word error rate  [BOLD] Voicesearch-', '[BOLD] Word error rate  [BOLD] Call-', '[BOLD] Word error rate  [BOLD] YouTube']
Next, we look at performance of application domain specific models. Results are shown in Tab. We present results using 3 domain-specific models trained on Voicesearch and Dictation, YouTube, and Call-Center data, respectively. Unsurprisingly, the models work well when the test domains match the model domains, and poorly when the domain changes. The YouTube model, which is trained with the largest dataset among the 3 models, generalizes better. This is most likely because YouTube training set includes data from a varied set of sources. Comparing these results with those in Tab. It also generalizes better: It has better performance on the Telephony set compared to the domain-specific models, and also on the noisy test sets compared to the baseline model trained without noise simulation. Finally, on the Call-center set, it outperforms the domain-specific model, which shows that multidomain training partly addresses data sparsity issues.