论文标题
TED-LIUM版本2的RWTH ASR系统：通过Specaugment改善混合HMM
The RWTH ASR System for TED-LIUM Release 2: Improving Hybrid HMM with SpecAugment
论文作者
论文摘要
我们提出了一条完整的培训管道，以在TED-Lium Copus的第二版中建立最先进的基于HMM的ASR系统。使用Specaught的数据扩展已成功应用于提高使用I-Vector的最佳SAT模型的性能。通过研究不同掩膜的效果，我们可以通过对混合HMM模型的规格进行改进，而不会增加模型大小和训练时间。随后的SMBR训练将用于微调最终声学模型，并对LSTM和Transformer语言模型进行了训练和评估。我们的最佳系统在测试集上实现了5.6％的速度，这表现优于先前的最新相对相对27％。
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.
