Speech production knowledge in automatic speech recognition S King, J Frankel, K Livescu, E McDermott, K Richmond, M Wester The Journal of the Acoustical Society of America 121 (2), 723-742, 2007 | 253 | 2007 |
Multisyn: Open-domain unit selection for the Festival speech synthesis system RAJ Clark, K Richmond, S King Speech Communication 49 (4), 317-330, 2007 | 206 | 2007 |
Phonology impacts segmentation in online speech processing L Onnis, P Monaghan, K Richmond, N Chater Journal of Memory and Language 53 (2), 225-237, 2005 | 185 | 2005 |
Announcing the electromagnetic articulography (day 1) subset of the mngu0 articulatory corpus K Richmond, P Hoole, S King Twelfth Annual Conference of the International Speech Communication Association, 2011 | 162 | 2011 |
Estimating articulatory parameters from the acoustic speech signal K Richmond The University of Edinburgh, 2002 | 142 | 2002 |
Festival 2–build your own general purpose unit selection speech synthesiser RAJ Clark, K Richmond, S King | 133 | 2004 |
Integrating articulatory features into HMM-based parametric speech synthesis ZH Ling, K Richmond, J Yamagishi, RH Wang IEEE Transactions on Audio, Speech, and Language Processing 17 (6), 1171-1185, 2009 | 126 | 2009 |
A trajectory mixture density network for the acoustic-articulatory inversion mapping K Richmond Ninth International Conference on Spoken Language Processing, 2006 | 121 | 2006 |
Modelling the uncertainty in recovering articulation from acoustics K Richmond, S King, P Taylor Computer Speech & Language 17 (2-3), 153-172, 2003 | 119 | 2003 |
Continuous speech recognition using articulatory data A Wrench, K Richmond International Speech Communication Association, 2000 | 118 | 2000 |
Attentive filtering networks for audio replay attack detection CI Lai, A Abad, K Richmond, J Yamagishi, N Dehak, S King ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and …, 2019 | 94 | 2019 |
Deep architectures for articulatory inversion B Uria, I Murray, S Renals, K Richmond INTERSPEECH 2012 13th Annual Conference of the International Speech …, 2012 | 83 | 2012 |
Towards an improved modeling of the glottal source in statistical parametric speech synthesis JP Cabral, S Renals, K Richmond, J Yamagishi | 81 | 2007 |
Articulatory control of HMM-based parametric speech synthesis using feature-space-switched multiple regression ZH Ling, K Richmond, J Yamagishi IEEE Transactions on Audio, Speech, and Language Processing 21 (1), 207-219, 2012 | 74 | 2012 |
An automatic speech recognition system using neural networks and linear dynamic models to recover and model articulatory traces J Frankel, K Richmond, S King, P Taylor International Speech Communication Association, 2000 | 68 | 2000 |
Speech recognition via phonetically-featured syllables S King, P Taylor, J Frankel, K Richmond University of the Saarland, 2000 | 67 | 2000 |
Glottal spectral separation for parametric speech synthesis. JP Cabral, S Renals, K Richmond, J Yamagishi Interspeech, 1829-1832, 2008 | 66 | 2008 |
Trajectory mixture density networks with multiple mixtures for acoustic-articulatory inversion K Richmond Advances in Nonlinear Speech Processing, 263-272, 2007 | 66 | 2007 |
A deep neural network for acoustic-articulatory speech inversion B Uria, S Renals, K Richmond NIPS 2011 Workshop on Deep Learning and Unsupervised Feature Learning, 1-9, 2011 | 62 | 2011 |
An analysis of HMM-based prediction of articulatory movements ZH Ling, K Richmond, J Yamagishi Speech Communication 52 (10), 834-846, 2010 | 62 | 2010 |