| Statistical speech enhancement based on probabilistic integration of variational autoencoder and non-negative matrix factorization Y Bando, M Mimura, K Itoyama, K Yoshii, T Kawahara 2018 IEEE International Conference on Acoustics, Speech and Signal …, 2018 | 155 | 2018 |
| Acoustic-to-word attention-based model complemented with character-level CTC-based model S Ueno, H Inaguma, M Mimura, T Kawahara 2018 IEEE International Conference on Acoustics, Speech and Signal …, 2018 | 78 | 2018 |
| Leveraging sequence-to-sequence speech synthesis for enhancing acoustic-to-word speech recognition M Mimura, S Ueno, H Inaguma, S Sakai, T Kawahara 2018 IEEE Spoken Language Technology Workshop (SLT), 477-484, 2018 | 74 | 2018 |
| Unsupervised speech enhancement based on multichannel NMF-informed beamforming for noise-robust automatic speech recognition K Shimada, Y Bando, M Mimura, K Itoyama, K Yoshii, T Kawahara IEEE/ACM Transactions on Audio, Speech, and Language Processing 27 (5), 960-971, 2019 | 66 | 2019 |
| Distilling the knowledge of BERT for sequence-to-sequence ASR H Futami, H Inaguma, S Ueno, M Mimura, S Sakai, T Kawahara arXiv preprint arXiv:2008.03822, 2020 | 63 | 2020 |
| Uyghur morpheme-based language models and ASR M Ablimit, G Neubig, M Mimura, S Mori, T Kawahara, A Hamdulla IEEE 10th International Conference on Signal Processing Proceedings, 581-584, 2010 | 48 | 2010 |
| Bayesian learning of a language model from continuous speech G Neubig, M Mimura, S Mori, T Kawahara IEICE TRANSACTIONS on Information and Systems 95 (2), 614-625, 2012 | 47 | 2012 |
| Learning a language model from continuous speech G Neubig, M Mimura, S Mori, T Kawahara Eleventh Annual Conference of the International Speech Communication Association, 2010 | 46 | 2010 |
| Multi-speaker sequence-to-sequence speech synthesis for data augmentation in acoustic-to-word speech recognition S Ueno, M Mimura, S Sakai, T Kawahara ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and …, 2019 | 43 | 2019 |
| Enhancing monotonic multihead attention for streaming asr H Inaguma, M Mimura, T Kawahara arXiv preprint arXiv:2005.09394, 2020 | 41 | 2020 |
| Continuous speech recognition consortium: an open repository for CSR tools and models A Lee, T Kawahara, K Takeda, M Mimura, A Yamada, A Ito, K Itou, ... | 35 | 2002 |
| Cross-domain speech recognition using nonparallel corpora with cycle-consistent adversarial networks M Mimura, S Sakai, T Kawahara 2017 IEEE automatic speech recognition and understanding workshop (ASRU …, 2017 | 34 | 2017 |
| Joint optimization of denoising autoencoder and dnn acoustic model based on multi-target learning for noisy speech recognition M Mimura, S Sakai, T Kawahara Proc. Interspeech 2016, 3803-3807, 2016 | 30 | 2016 |
| Speech dereverberation using long short-term memory M Mimura, S Sakai, T Kawahara Sixteenth Annual Conference of the International Speech Communication …, 2015 | 29 | 2015 |
| Automatic transcription system for meetings of the Japanese national congress Y Akita, M Mimura, T Kawahara Proc. InterSpeech 2009, 84-87, 2009 | 28 | 2009 |
| Data augmentation for asr using tts via a discrete representation S Ueno, M Mimura, S Sakai, T Kawahara 2021 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU), 68-75, 2021 | 27 | 2021 |
| Reverberant speech recognition combining deep neural networks and deep autoencoders augmented with a phone-class feature M Mimura, S Sakai, T Kawahara EURASIP journal on Advances in Signal Processing 2015, 1-13, 2015 | 25 | 2015 |
| Asr rescoring and confidence estimation with electra H Futami, H Inaguma, M Mimura, S Sakai, T Kawahara 2021 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU …, 2021 | 24 | 2021 |
| Semi-supervised ensemble DNN acoustic model training S Li, X Lu, S Sakai, M Mimura, T Kawahara 2017 IEEE International Conference on Acoustics, Speech and Signal …, 2017 | 22 | 2017 |
| Reverberant speech recognition combining deep neural networks and deep autoencoders M Mimura, S Sakai, T Kawahara IEEE REVERB Workshop, 2014 | 22 | 2014 |
