| Adaptation of deep neural network acoustic models using factorised i-vectors. P Karanasou, Y Wang, MJF Gales, PC Woodland Interspeech 2014, 2180-2184, 2014 | 83 | 2014 |
| Improving interpretability and regularization in deep learning C Wu, MJF Gales, A Ragni, P Karanasou, KC Sim IEEE/ACM Transactions on Audio, Speech, and Language Processing 26 (2), 256-265, 2017 | 45 | 2017 |
| Stimulated Deep Neural Network for Speech Recognition C Wu, P Karanasou, MJF Gales, KC Sim Interspeech, 400-404, 2016 | 44 | 2016 |
| Cambridge university transcription systems for the multi-genre broadcast challenge PC Woodland, X Liu, Y Qian, C Zhang, MJF Gales, P Karanasou, ... 2015 IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU …, 2015 | 41 | 2015 |
| Camp: a two-stage approach to modelling prosody in context Z Hodari, A Moinet, S Karlapati, J Lorenzo-Trueba, T Merritt, A Joly, ... ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and …, 2021 | 32 | 2021 |
| Cross-lingual Transfer Learning for Japanese Named Entity Recognition A Johnson, P Karanasou, J Gaspers, D Klakow Proceedings of NAACL-HLT 2019, 182–189, 2019 | 29 | 2019 |
| An investigation into speaker informed DNN front-end for LVCSR Y Liu, P Karanasou, T Hain 2015 IEEE International Conference on Acoustics, Speech and Signal …, 2015 | 26 | 2015 |
| Selecting Machine-Translated Data for Quick Bootstrapping of a Natural Language Understanding System J Gaspers, P Karanasou, R Chatterjee Proceedings of NAACL-HLT 2018, 137–144, 2018 | 24 | 2018 |
| Comparing SMT Methods for Automatic Generation of Pronunciation Variants P Karanasou, L Lamel International Conference on Natural Language Processing, 167-178, 2010 | 22 | 2010 |
| Prosodic representation learning and contextual sampling for neural text-to-speech S Karlapati, A Abbas, Z Hodari, A Moinet, A Joly, P Karanasou, ... ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and …, 2021 | 21 | 2021 |
| Selection of Multi-Genre Broadcast Data for the Training of Automatic Speech Recognition Systems P Lanchantin, MJF Gales, P Karanasou, X Liu, Y Qian, L Wang, ... Interspeech 2016, 3057-3061, 2016 | 21 | 2016 |
| Discriminatively trained phoneme confusion model for keyword spotting P Karanasou, L Burget, D Vergyri, M Akbacak, A Mandal INTERSPEECH, 2434-2437, 2012 | 21 | 2012 |
| Improved DNN-based segmentation for multi-genre broadcast audio L Wang, C Zhang, PC Woodland, MJF Gales, P Karanasou, P Lanchantin, ... 2016 IEEE International Conference on Acoustics, Speech and Signal …, 2016 | 20 | 2016 |
| Speaker diarisation and longitudinal linking in multi-genre broadcast data P Karanasou, MJF Gales, P Lanchantin, X Liu, Y Qian, L Wang, ... 2015 IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU …, 2015 | 19 | 2015 |
| The development of the Cambridge University alignment systems for the Multi-Genre Broadcast challenge P Lanchantin, MJF Gales, P Karanasou, X Liu, Y Qian, L Wang, ... 2015 IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU …, 2015 | 19 | 2015 |
| Discriminative training of a phoneme confusion model for a dynamic lexicon in ASR P Karanasou, F Yvon, T Lavergne, L Lamel INTERSPEECH, 1966-1970, 2013 | 16 | 2013 |
| Simple and effective multi-sentence TTS with expressive and coherent prosody P Makarov, A Abbas, M Łajszczak, A Joly, S Karlapati, A Moinet, ... arXiv preprint arXiv:2206.14643, 2022 | 15 | 2022 |
| CopyCat2: A single model for multi-speaker TTS and many-to-many fine-grained prosody transfer S Karlapati, P Karanasou, M Lajszczak, A Abbas, A Moinet, P Makarov, ... arXiv preprint arXiv:2206.13443, 2022 | 13 | 2022 |
| I-vector estimation using informative priors for adaptation of deep neural networks P Karanasou, M Gales, P Woodland ISCA, 2015 | 11 | 2015 |
| Phonemic variability and confusability in pronunciation modeling for automatic speech recognition P Karanasou Doctoral dissertation, Université Paris Sud-Paris XI, 2013 | 11 | 2013 |
Mark GalesCambridge University在 eng.cam.ac.uk 的电子邮件经过验证
