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Neural transducers provide a natural way of streaming ASR.
1990
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2018
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C. Wang, Y. Wu, L. Lu, S. Liu, J. Li, G. Ye, and M. Zhou, “Low latency end-to-end streaming speech recognition with a scout network,” in Proc. Interspeech
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J. Li, Y. Wu, Y. Gaur, C. Wang, R. Zhao, and S. Liu, “On the comparison of popular end-to-end models for large scale speech recognition,” in Proc. Interspeech
2020
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M. Ghodsi, X. Liu, J. Apfel, R. Cabrera, and E. Weinstein, “RNN-transducer with stateless prediction network,” in Proc. ICASSP
2020
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L. Dong and B. Xu, “CIF: Continuous integrate-and-fire for end-to-end speech recognition,” in Proc. ICASSP
2020
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E. Variani, D. Rybach, C. Allauzen, and M. Riley, “Hybrid autoregressive transducer (HAT),” in Proc. ICASSP
2020
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Q. Zhang, H. Lu, H. Sak, A. Tripathi, E. McDermott, S. Koo, and S. Kumar, “Transformer transducer: A streamable speech recognition model with transformer encoders and RNN-T loss,” in Proc. ICASSP
2020
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E. Tsunoo, Y. Kashiwagi, and S. Watanabe, “Streaming transformer ASR with blockwise synchronous beam search,” in Proc. SLT
2021
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X. Chen, Z. Meng, S. Parthasarathy, and J. Li, “Factorized neural transducer for efficient language model adaptation,” in Proc. ICASSP
2022
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Z. Meng, Y. Gaur, N. Kanda, J. Li, X. Chen, Y. Wu, and Y. Gong, “Internal language model adaptation with text-only data for end-to-end speech recognition,” in Proc. Interspeech
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Z. Meng, N. Kanda, Y. Gaur, S. Parthasarathy, E. Sun, L. Lu, X. Chen, J. Li, and Y. Gong, “Internal language model training for domain-adaptive end-to-end speech recognition,” in Proc. ICASSP
2021
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M. Zeineldeen, A. Glushko, W. Michel, A. Zeyer, R. Schlüter, and H. Ney, “Investigating methods to improve language model integration for attention-based encoder-decoder ASR models,” in Proc. Interspeech
2021
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Z. Meng, S. Parthasarathy, E. Sun, Y. Gaur, N. Kanda, L. Lu, X. Chen, R. Zhao, J. Li, and Y. Gong, “Internal language model estimation for domain-adaptive end-to-end speech recognition,” in Proc. SLT
2021
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X. Zheng, Y. Liu, D. Gunceler, and D. Willett, “Using synthetic audio to improve the recognition of out-of-vocabulary words in end-to-end ASR systems,” Proc. ICASSP
2021
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C. Yi, S. Zhou, and B. Xu, “Efficiently fusing pretrained acoustic and linguistic encoders for low-resource speech recognition,” IEEE Signal Process. Lett
2021
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X. Shi, F. Yu, Y. Lu, Y. Liang, Q. Feng, D. Wang, Y. Qian, and L. Xie, “The accented English speech recognition challenge 2020: Open datasets, tracks, baselines, results and methods,” in Proc. ICASSP
2021
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W.-N. Hsu, A. Sriram, A. Baevski, T. Likhomanenko, Q. Xu, V. Pratap, J. Kahn, A. Lee, R. Collobert, G. Synnaeve, and M. Auli, “Robust wav2vec 2.0: Analyzing domain shift in self-supervised pre-training,” in Proc. Interspeech
2021
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W. Zhou, Z. Zheng, R. Schlüter, and H. Ney, “On language model integration for RNN transducer based speech recognition,” in Proc. ICASSP
2022
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2022
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K. Deng, S. Cao, Y. Zhang, L. Ma, G. Cheng, J. Xu, and P. Zhang, “Improving CTC-based speech recognition via knowledge transferring from pre-trained language models,” Proc. ICASSP
2022
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X. Yang, Q. Li, and P. C. Woodland, “Knowledge distillation for neural transducers from large self-supervised pre-trained models,” in Proc. ICASSP
2022
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D. Albesano, J. Andrés-Ferrer, N. Ferri, and P. Zhan, “On the prediction network architecture in RNN-T for ASR,” in Proc. Interspeech
2022
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Z. Meng, T. Chen, R. Prabhavalkar, Y. Zhang, G. Wang, K. Audhkhasi, J. Emond, T. Strohman, B. Ramabhadran, W. R. Huang, E. Variani, Y. Huang, and P. J. Moreno, “Modular hybrid autoregressive transducer,” in Proc. SLT
2023
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