Toward domain-invariant speech recognition via large scale training
Table 3: Results using models trained w/ and w/o noise.
['[BOLD] Test set', '[BOLD] Sample  [BOLD] rate', '[BOLD] Word error rate  [BOLD] w/o Noise', '[BOLD] Word error rate  [BOLD] w/ Noise']
4.3.1 Noise To evaluate noise simulation , we train using Voicesearch, Dictation and Other search sets, with and without simulated background noise. Results are shown in Tab. As shown, training with simulated noisy data using the MTR settings described in Sec. The word error rates (WERs) in clean conditions are almost identical when using models trained with and without noise. Unsurprisingly, training with noise significantly improves performance in noisy conditions. For the Dictation set, MTR training improves WER by around 60% (relative). It is also interesting to note that MTR training only marginally affects performance of the 8 kHz test set. When doing noise simulation, we selectively add noise to only those domains for which the input data is relatively clean. Specifically, we add noise only to Voicesearch, Dictation, and Call-center. When the multidomain model is trained with noise simulation, it performs better on the noisy sets but degrades on the clean sets. Moreover, the performance in noisy conditions doesn’t match those of the domain-specific model trained with noise (Tab. This is likely because the model doesn’t see as much clean and noisy data as the domain-specific models because of the imbalance in training data distribution. Adding noise also makes the underlying task harder since the model has to learn to normalize out variations in the training data along several dimensions like application domain, noise and sample rate. It is interesting to see that even though large scale training works in some conditions, combinations of conditions makes it harder for the model to learn without any additional signal during training. As with noise, the multidomain model with codec simulation shows degradation on clean and noisy tests without codecs, as well as for the AAC 23k condition. It works better only on MP3 23k condition. In contrast, the domain-specific codec model performed better than its non-codec counterpart all around.