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| | | FunASR have implemented the following paper code |
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| | | ### Speech Recognition Models |
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| | | ### Speech Recognition |
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| | | - [Paraformer: Fast and Accurate Parallel Transformer for Non-autoregressive End-to-End Speech Recognition](https://arxiv.org/abs/2206.08317), INTERSPEECH 2022. |
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| | | - [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. |
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| | | - [San-m: Memory equipped self-attention for end-to-end speech recognition](https://arxiv.org/pdf/2006.01713), INTERSPEECH 2020 |
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| | | - [Sequence-to-sequence learning with Transducers](https://arxiv.org/pdf/1211.3711.pdf), NIPS 2016 |
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| | | ### Multi-talker Speech Recognition Models |
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| | | ### Multi-talker Speech Recognition |
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| | | - [MFCCA:Multi-Frame Cross-Channel attention for multi-speaker ASR in Multi-party meeting scenario](https://arxiv.org/abs/2210.05265), ICASSP 2022 |
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| | | ### Voice Activity Detection |
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| | | ### Speaker diarization |
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| | | - [Speaker Overlap-aware Neural Diarization for Multi-party Meeting Analysis](https://arxiv.org/abs/2211.10243), EMNLP 2022 |
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| | | - [TOLD: A Novel Two-Stage Overlap-Aware Framework for Speaker Diarization](https://arxiv.org/abs/2303.05397), ICASSP 2023 |
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| | | ### Timestamp Prediction |
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| | | - [Achieving Timestamp Prediction While Recognizing with Non-Autoregressive End-to-End ASR Model](https://arxiv.org/abs/2301.12343), arXiv:2301.12343 |
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