Követés
Cassia Valentini-Botinhao
Cassia Valentini-Botinhao
E-mail megerősítve itt: inf.ed.ac.uk
Cím
Hivatkozott rá
Hivatkozott rá
Év
Investigating RNN-based speech enhancement methods for noise-robust text-to-speech
CV Botinhao, X Wang, S Takaki, J Yamagishi
9th ISCA speech synthesis workshop, 159-165, 2016
4872016
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
3412015
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
2132017
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
1502013
Speech enhancement for a noise-robust text-to-speech synthesis system using deep recurrent neural networks
CV Botinhao, X Wang, S Takaki, J Yamagishi
Interspeech 2016, 352-356, 2016
1422016
Intelligibility-enhancing speech modifications: the Hurricane Challenge
M Cooke, C Mayo, C Valentini-Botinhao
Interspeech, Lyon, France, 2013
1402013
Are we using enough listeners? No! An empirically-supported critique of Interspeech 2014 TTS evaluations
M Wester, C Valentini-Botinhao, GE Henter
Interspeech 2015, 3476-3480, 2015
742015
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
602015
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
492014
Can objective measures predict the intelligibility of modified HMM-based synthetic speech in noise?
C Valentini-Botinhao, J Yamagishi, S King
Interspeech, 2011
492011
Speech enhancement of noisy and reverberant speech for text-to-speech
C Valentini-Botinhao, J Yamagishi
IEEE/ACM Transactions on Audio, Speech, and Language Processing 26 (8), 1420 …, 2018
482018
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
392015
Direct modelling of magnitude and phase spectra for statistical parametric speech synthesis
CV Botinhao, S King
Interspeech 2017, 2017
362017
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
362014
Where do the improvements come from in sequence-to-sequence neural TTS?
O Watts, GE Henter, J Fong, C Valentini-Botinhao
2019 ISCA speech synthesis workshop (SSW) 10, 217-222, 2019
352019
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
332016
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
332011
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
312012
Intelligibility-Enhancing Speech Modifications-The Hurricane Challenge 2.0.
J Rennies, HF Schepker, C Valentini-Botinhao, M Cooke
INTERSPEECH, 1341-1345, 2020
302020
Normal-to-Lombard adaptation of speech synthesis using long short-term memory recurrent neural networks
B Bollepalli, L Juvela, M Airaksinen, C Valentini-Botinhao, P Alku
Speech Communication 110, 64-75, 2019
282019
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Cikkek 1–20