Generating Synthetic Audio Data for Attention-BasedSpeech Recognition Systems
Table 3: Results on LibriSpeech-100 comparing synthetic data generated with a TTS system using GST or i-vector embeddings against the oracle data. All results are with an additional LM (LM-large).
['Spec Aug', 'Syn. Audio', 'WER[%] dev', 'WER[%] dev', 'WER[%] test', 'WER[%] test']
The GST-based system clearly outperforms the TTS system using i-vector representations. We see that the relative improvement of using synthetic data gets even larger when using LM-large and SpecAugment, up to a performance increase of 33%. The amount of presented audio during training is about 4000 hours, corresponding to ∼12.5 epochs original data (100h) and 8.5 epochs synthetic data (330h).