| Deep Neural Networks employing multi-task learning and stacked bottleneck features for speech synthesis Z Wu, C Valentini-Botinhao, O Watts, S King ICASSP, 2015 | 268 | 2015 |
| Evaluating the intelligibility benefit of speech modifications in known noise conditions M Cooke, C Mayo, C Valentini-Botinhao, Y Stylianou, B Sauert, Y Tang Speech Communication 55 (4), 572-585, 2013 | 111 | 2013 |
| Intelligibility-enhancing speech modifications: the Hurricane Challenge M Cooke, C Mayo, C Valentini-Botinhao Interspeech, Lyon, France, 2013 | 97 | 2013 |
| Investigating RNN-based speech enhancement methods for noise-robust Text-to-Speech. C Valentini-Botinhao, X Wang, S Takaki, J Yamagishi SSW, 146-152, 2016 | 95 | 2016 |
| A deep generative architecture for postfiltering in statistical parametric speech synthesis LH Chen, T Raitio, C Valentini-Botinhao, ZH Ling, J Yamagishi IEEE/ACM Transactions on Audio, Speech, and Language Processing 23 (11 …, 2015 | 50 | 2015 |
| Can objective measures predict the intelligibility of modified HMM-based synthetic speech in noise? C Valentini-Botinhao, J Yamagishi, S King Interspeech, 2011 | 40 | 2011 |
| DNN-Based Stochastic Postfilter for HMM-Based Speech Synthesis LH Chen, T Raitio, C Valentini-Botinhao, J Yamagishi, ZH Ling Fifteenth Annual Conference of the International Speech Communication …, 2014 | 39 | 2014 |
| Modelling acoustic feature dependencies with artificial neural networks: Trajectory-RNADE B Uria, I Murray, S Renals, C Valentini-Botinhao, J Bridle 2015 IEEE International Conference on Acoustics, Speech and Signal …, 2015 | 36 | 2015 |
| Speech Enhancement for a Noise-Robust Text-to-Speech Synthesis System Using Deep Recurrent Neural Networks. C Valentini-Botinhao, X Wang, S Takaki, J Yamagishi Interspeech, 352-356, 2016 | 35 | 2016 |
| Noisy speech database for training speech enhancement algorithms and TTS models C Valentini-Botinhao University of Edinburgh. School of Informatics. Centre for Speech Technology …, 2017 | 29 | 2017 |
| Are we using enough listeners? No!—an empirically-supported critique of Interspeech 2014 TTS evaluations M Wester, C Valentini-Botinhao, GE Henter Sixteenth Annual Conference of the International Speech Communication …, 2015 | 29 | 2015 |
| Intelligibility enhancement of HMM-generated speech in additive noise by modifying Mel cepstral coefficients to increase the glimpse proportion C Valentini-Botinhao, J Yamagishi, S King, R Maia Computer Speech & Language 28 (2), 665-686, 2014 | 28 | 2014 |
| Mel cepstral coefficient modification based on the Glimpse Proportion measure for improving the intelligibility of HMM-generated synthetic speech in noise C Valentini-Botinhao, J Yamagishi, S King Interspeech, 2012 | 28 | 2012 |
| Evaluation of objective measures for intelligibility prediction of HMM-based synthetic speech in noise C Valentini-Botinhao, J Yamagishi, S King ICASSP, 5112-5115, 2011 | 26 | 2011 |
| Direct Modelling of Magnitude and Phase Spectra for Statistical Parametric Speech Synthesis. F Espic, C Valentini-Botinhao, S King INTERSPEECH, 1383-1387, 2017 | 23 | 2017 |
| Towards minimum perceptual error training for DNN-based speech synthesis C Valentini-Botinhao, Z Wu, S King Sixteenth Annual Conference of the International Speech Communication …, 2015 | 22 | 2015 |
| Cepstral analysis based on the Glimpse proportion measure for improving the intelligibility of HMM-based synthetic speech in noise C Valentini-Botinhao, R Maia, J Yamagishi, S King, H Zen ICASSP, 3997-4000, 2012 | 21 | 2012 |
| Evaluating the predictions of objective intelligibility metrics for modified and synthetic speech Y Tang, M Cooke, C Valentini-Botinhao Computer Speech & Language 35, 73-92, 2016 | 20 | 2016 |
| Hurricane natural speech corpus M Cooke, C Mayo, C Valentini-Botinhao LISTA Consortium:(i) Language and Speech Laboratory, Universidad del Pais …, 2013 | 12 | 2013 |
| Improving intelligibility in noise of HMM-generated speech via noise-dependent and -independent methods C Valentini-Botinhao, E Godoy, Y Stylianou, B Sauert, S King, ... ICASSP, 2013 | 12 | 2013 |
Simon KingProfessor of Speech Processing, University of EdinburghVerified email at ed.ac.uk
