The Rwth Asr System for Ted-Lium Release 2: Improving Hybrid Hmm With Specaugment

By Wei Zhou, Wilfried Michel, Kazuki Irie, Markus Kitza, Ralf Schlüter, Hermann Ney. 2020

46 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Speech & Audio

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Citations in the corpus

Robust wav2vec 2.0: Analyzing Domain Shift in Self-Supervised Pre-Training (2021, methodology): “Our model is not pre-trained or fine-tuned on any TD data, and when fine-tuning it with LS, it achieves better performance on TD than the supervised SOTA trained on TD [46] as well as the joint RASR model trained on five labeled datasets including TD [8].”
Rethinking Evaluation in ASR: Are Our Models Robust Enough? (2020, methodology): “TED-LIUM v3 (TL) [19] is based on TED conference videos.”
End-to-End Speech Recognition: A Survey (2023, methodology): “SpecAugment also is shown to improve classical hybrid HMM models [297].”