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We present a complete training pipeline to build a state-of-the-art hybridHMM-based ASR system on the 2nd release of the TED-LIUM corpus. Dataaugmentation using SpecAugment is successfully applied to improve performanceon top of our best SAT model using i-vectors. By investigating the effect ofdifferent maskings, we achieve improvements from SpecAugment on hybrid HMMmodels without increasing model size and training time. A subsequent sMBRtraining is applied to fine-tune the final acoustic model, and both LSTM andTransformer language models are trained and evaluated. Our best system achievesa 5.6% WER on the test set, which outperforms the previous state-of-the-art by27% relative.

The RWTH ASR System for TED-LIUM Release 2: Improving Hybrid HMM with SpecAugment