diff --git a/README.md b/README.md
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@@ -15,6 +15,7 @@ WER are we? An attempt at tracking states of the art(s) and recent results on sp
 | 1.9% | 3.9% | [Conformer: Convolution-augmented Transformer for Speech Recognition](https://arxiv.org/pdf/2005.08100.pdf) | May 2020 | Convolution-augmented-Transformer(Conformer) + 3-layer LSTM LM (data augmentation:SpecAugment) |
 | 1.9%  | 4.1%   | [ContextNet: Improving Convolutional Neural Networks for Automatic Speech Recognition with Global Context](https://arxiv.org/pdf/2005.03191.pdf) | May 2020 | CNN-RNN-Transducer(ContextNet) + 3-layer LSTM LM (data augmentation:SpecAugment)  |
 | 2.0%  | 4.1%   | [End-to-end ASR: from Supervised to Semi-Supervised Learning with Modern Architectures](https://arxiv.org/abs/1911.08460) | November 2019 | Conv+Transformer AM (10k word pieces) with ConvLM decoding and Transformer rescoring + 60k hours unlabeled |
+| 2.1%  | 4.1%   | [Efficient Training of Neural Transducer for Speech Recognition](https://arxiv.org/abs/2204.10586) | May 2022 | Conformer Transducer (efficient 3-stage progressive training: 35 epochs in total) + Transformer LM |
 | 2.3% | 4.9%  | [Transformer-based Acoustic Modeling for Hybrid Speech Recognition](https://arxiv.org/abs/1910.09799)  | October 2019 | Transformer AM (chenones) + 4-gram LM + Neural LM rescore (data augmentation:Speed perturbation and SpecAugment) |
 | 2.3%  | 5.0%   | [RWTH ASR Systems for LibriSpeech: Hybrid vs Attention](https://arxiv.org/abs/1905.03072) | September 2019, Interspeech | HMM-DNN + lattice-based sMBR + LSTM LM + Transformer LM rescoring (no data augmentation) |
 | 2.3%  | 5.2%   | [End-to-end ASR: from Supervised to Semi-Supervised Learning with Modern Architectures](https://arxiv.org/abs/1911.08460) | November 2019 | Conv+Transformer AM (10k word pieces) with ConvLM decoding and Transformer rescoring |