docs

docs/model_zoo/modelscope_models.md

@@ -54,6 +54,7 @@ Here we provided several pretrained models on different datasets. The details of
 
 
 #### Conformer
+
 |                                                       Model Name                                                       | Language |     Training Data     | Vocab Size | Parameter | Offline/Online | Notes                                                                                                                           |
 |:----------------------------------------------------------------------------------------------------------------------:|:--------:|:---------------------:|:----------:|:---------:|:--------------:|:--------------------------------------------------------------------------------------------------------------------------------|
 | [Conformer](https://modelscope.cn/models/damo/speech_conformer_asr_nat-zh-cn-16k-aishell1-vocab4234-pytorch/summary)   |   CN     |  AISHELL (178hours)   |    4234    |    44M    |    Offline     | Duration of input wav <= 20s                                                                                                    |

docs/reference/papers.md

@@ -6,7 +6,9 @@ FunASR have implemented the following paper code
 - [FunASR: A Fundamental End-to-End Speech Recognition Toolkit](https://arxiv.org/abs/2305.11013), INTERSPEECH 2023
 - [BAT: Boundary aware transducer for memory-efficient and low-latency ASR](https://arxiv.org/abs/2305.11571), INTERSPEECH 2023
 - [Paraformer: Fast and Accurate Parallel Transformer for Non-autoregressive End-to-End Speech Recognition](https://arxiv.org/abs/2206.08317), INTERSPEECH 2022
-- [Universal ASR: Unifying Streaming and Non-Streaming ASR Using a Single Encoder-Decoder Model](https://arxiv.org/abs/2010.14099), arXiv preprint arXiv:2010.14099, 2020.
+- [E-branchformer: Branchformer with enhanced merging for speech recognition](https://arxiv.org/abs/2210.00077), SLT 2022
+- [Branchformer: Parallel mlp-attention architectures to capture local and global context for speech recognition and understanding](https://proceedings.mlr.press/v162/peng22a.html?ref=https://githubhelp.com), ICML 2022
+- [Universal ASR: Unifying Streaming and Non-Streaming ASR Using a Single Encoder-Decoder Model](https://arxiv.org/abs/2010.14099), arXiv preprint arXiv:2010.14099, 2020
 - [San-m: Memory equipped self-attention for end-to-end speech recognition](https://arxiv.org/pdf/2006.01713), INTERSPEECH 2020
 - [Streaming Chunk-Aware Multihead Attention for Online End-to-End Speech Recognition](https://arxiv.org/abs/2006.01712), INTERSPEECH 2020
 - [Conformer: Convolution-augmented Transformer for Speech Recognition](https://arxiv.org/abs/2005.08100),  INTERSPEECH 2020