  We present a complete training pipeline to build a state-of-the-art hybrid
HMM-based ASR system on the 2nd release of the TED-LIUM corpus. Data
augmentation using SpecAugment is successfully applied to improve performance
on top of our best SAT model using i-vectors. By investigating the effect of
different maskings, we achieve improvements from SpecAugment on hybrid HMM
models without increasing model size and training time. A subsequent sMBR
training is applied to fine-tune the final acoustic model, and both LSTM and
Transformer language models are trained and evaluated. Our best system achieves
a 5.6% WER on the test set, which outperforms the previous state-of-the-art by
27% relative.
