Generating Synthetic Audio Data for Attention-BasedSpeech Recognition Systems
Table 4: Results on LibriSpeech-960 comparing synthetic data generated with a TTS system using GST or i-vector embeddings against the oracle data. In contrast to [11], our results include SpecAugment and an LM (LM-large) in decoding.
['Paper', 'Retrain', 'Syn. Audio', 'WER[%] dev', 'WER[%] dev', 'WER[%] test', 'WER[%] test']
In preliminary experiments we also tested if we can improve our currently best baseline setups for LibriSpeech-960h using the TTS models trained on LibriSpeech-100h. For the i-vector model we used representations computed on the full corpus, but the GST model is exactly the same as in the 100h case. We generated 2000 hours of additional data with the TTS models using the language model text-data as input, and used the on-the-fly feature augmentation to train the models for 12,500 hours of augmented data. As the models converged slower and showed less overfitting when using synthetic data, we used an original to synthetic data ratio of 3 to 2 and extended the training time by re-training each model for another 12,500 hours of training data (∼15 epochs on the original data and ∼5 epochs on the synthetic data). The relative improvements are not exceeding 5% WER, and we assume further investigation is needed to balance the regularization effects of Dropout, SpecAugment and Synthetic Data.