| Time series classification with HIVE-COTE: The hierarchical vote collective of transformation-based ensembles J Lines, S Taylor, A Bagnall ACM Transactions on Knowledge Discovery from Data 12 (5), 2018 | 218 | 2018 |
| A deep learning approach for generalized speech animation S Taylor, T Kim, Y Yue, M Mahler, J Krahe, AG Rodriguez, J Hodgins, ... ACM Transactions on Graphics (TOG) 36 (4), 1-11, 2017 | 201 | 2017 |
| Dynamic Units of Visual Speech SL Taylor, M Mahler, BJ Theobald, I Matthews Eurographics/ACM SIGGRAPH Symposium on Computer Animation, 275-284, 2012 | 147 | 2012 |
| Hive-cote: The hierarchical vote collective of transformation-based ensembles for time series classification J Lines, S Taylor, A Bagnall 2016 IEEE 16th international conference on data mining (ICDM), 1041-1046, 2016 | 146 | 2016 |
| A decision tree framework for spatiotemporal sequence prediction T Kim, Y Yue, S Taylor, I Matthews Proceedings of the 21th ACM SIGKDD international conference on knowledge …, 2015 | 62 | 2015 |
| Comparison of human and machine-based lip-reading. S Hilder, RW Harvey, BJ Theobald AVSP, 86-89, 2009 | 43 | 2009 |
| Audio-to-Visual Speech Conversion Using Deep Neural Networks S Taylor, A Kato, I Matthews, B Milner Interspeech 2016}, 1482-1486, 2016 | 30 | 2016 |
| In pursuit of visemes S Hilder, BJ Theobald, R Harvey Auditory-Visual Speech Processing 2010, 2010 | 21 | 2010 |
| The effect of speaking rate on audio and visual speech S Taylor, BJ Theobald, I Matthews 2014 IEEE International Conference on Acoustics, Speech and Signal …, 2014 | 19 | 2014 |
| Generating a visually consistent alternative audio for redubbing visual speech I Matthews, S Taylor, BJ Theobald US Patent 9,922,665, 2018 | 14 | 2018 |
| Synthesising visual speech using dynamic visemes and deep learning architectures A Thangthai, B Milner, S Taylor Computer Speech & Language 55, 101-119, 2019 | 13 | 2019 |
| Self-supervised monocular depth estimation with internal feature fusion H Zhou, D Greenwood, S Taylor arXiv preprint arXiv:2110.09482, 2021 | 10 | 2021 |
| Visual speech recognition: aligning terminologies for better understanding HL Bear, S Taylor arXiv preprint arXiv:1710.01292, 2017 | 9 | 2017 |
| Systems and methods for speech animation using visemes with phonetic boundary context BJ Theobald, M Meyerhofer, I Matthews, S Taylor US Patent 9,911,218, 2018 | 8 | 2018 |
| A mouth full of words: Visually consistent acoustic redubbing S Taylor, BJ Theobald, I Matthews 2015 IEEE International Conference on Acoustics, Speech and Signal …, 2015 | 8 | 2015 |
| Constant velocity constraints for self-supervised monocular depth estimation H Zhou, D Greenwood, S Taylor, H Gong European Conference on Visual Media Production, 1-8, 2020 | 5 | 2020 |
| The effect of real-time constraints on automatic speech animation D Websdale, S Taylor, B Milner | 4 | 2018 |
| Visual speech synthesis using dynamic visemes, contextual features and DNNs A Thangthai, B Milner, S Taylor International Speech Communication Association, 2016 | 4 | 2016 |
| Speaker-independent speech animation using perceptual loss functions and synthetic data D Websdale, S Taylor, B Milner IEEE Transactions on Multimedia 24, 2539-2552, 2021 | 2 | 2021 |
| SUB-Depth: Self-distillation and Uncertainty Boosting Self-supervised Monocular Depth Estimation H Zhou, S Taylor, D Greenwood arXiv preprint arXiv:2111.09692, 2021 | 1 | 2021 |
