[1]
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Felipe Espic, Cassia Valentini-Botinhao, and Simon King.
Direct modelling of magnitude and phase spectra for statistical
parametric speech synthesis.
In Proc. Interspeech, Stochohlm, Sweden, August 2017.
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[2]
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Michael Pucher, Bettina Zillinger, Markus Toman, Dietmar Schabus, Cassia
Valentini-Botinhao, Junichi Yamagishi, Erich Schmid, and Thomas Woltron.
Influence of speaker familiarity on blind and visually impaired
children and young adults perception of synthetic voices.
Computer Speech and Language, 46:179-195, June 2017.
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[3]
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Cassia Valentini-Botinhao and Junichi Yamagishi.
Speech intelligibility in cars: the effect of speaking style, noise
and listener age.
In Interspeech, 2017.
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[4]
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Jaime Lorenzo-Trueba, Cassia Valentini-Botinhao, Gustav Henter, and Junichi
Yamagishi.
Misperceptions of the emotional content of natural and vocoded speech
in a car.
In Interspeech, 2017.
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[5]
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Cassia Valentini-Botinhao, Xin Wang, Shinji Takaki, and Junichi Yamagishi.
Speech enhancement for a noise-robust text-to-speech synthesis system
using deep recurrent neural networks.
In Interspeech, pages 352-356. ISCA, September 2016.
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[6]
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Cassia Valentini-Botinhao, Xin Wang, Shinji Takaki, and Junichi Yamagishi.
Investigating RNN-based speech enhancement methods for noise-robust
text-to-speech.
In Proceedings of 9th ISCA Speech Synthesis Workshop, pages
159-165, September 2016.
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[7]
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Felipe Espic, Cassia Valentini-Botinhao, Zhizheng Wu, and Simon King.
Waveform generation based on signal reshaping for statistical
parametric speech synthesis.
In Proc. Interspeech, pages 2263-2267, San Francisco, CA, USA,
September 2016.
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[8]
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Rasmus Dall, Sandrine Brognaux, Korin Richmond, Cassia Valentini-Botinhao,
Gustav Eje Henter, Julia Hirschberg, and Junichi Yamagishi.
Testing the consistency assumption: pronunciation variant forced
alignment in read and spontaneous speech synthesis.
In Proc. IEEE International Conference on Acoustics, Speech, and
Signal Processing (ICASSP), pages 5155-5159, March 2016.
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[9]
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Yan Tang, Martin Cooke, and Cassia Valentini-Botinhao.
Evaluating the predictions of objective intelligibility metrics for
modified and synthetic speech.
Computer Speech & Language, 35:73 - 92, 2016.
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[10]
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Adriana Stan, Cassia Valentini-Botinhao, Bogdan Orza, and Mircea Giurgiu.
Blind speech segmentation using spectrogram image-based features and
mel cepstral coefficients.
In SLT, pages 597-602. IEEE, 2016.
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[11]
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Cassia Valentini-Botinhao, Markus Toman, Michael Pucher, Dietmar Schabus, and
Junichi Yamagishi.
Intelligibility of time-compressed synthetic speech: Compression
method and speaking style.
Speech Communication, October 2015.
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[12]
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C. Valentini-Botinhao, Z. Wu, and S. King.
Towards minimum perceptual error training for DNN-based speech
synthesis.
In Proc. Interspeech, Dresden, Germany, September 2015.
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[13]
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M. Pucher, M. Toman, D. Schabus, C. Valentini-Botinhao, J. Yamagishi,
B. Zillinger, and E Schmid.
Influence of speaker familiarity on blind and visually impaired
children's perception of synthetic voices in audio games.
In Proc. Interspeech, Dresden, Germany, September 2015.
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[14]
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Mirjam Wester, Cassia Valentini-Botinhao, and Gustav Eje Henter.
Are we using enough listeners? No! An empirically-supported critique
of Interspeech 2014 TTS evaluations.
In Proc. Interspeech, pages 3476-3480, Dresden, September
2015.
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[15]
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Z. Wu, C. Valentini-Botinhao, O. Watts, and S. King.
Deep neural networks employing multi-task learning and stacked
bottleneck features for speech synthesis.
In Proc. ICASSP, pages 4460-4464, Brisbane, Australia, April
2015.
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[16]
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B. Uria, I. Murray, S. Renals, C. Valentini-Botinhao, and J. Bridle.
Modelling acoustic feature dependencies with artificial neural
networks: Trajectory-RNADE.
In Proc. ICASSP, pages 4465-4469, Brisbane, Australia, April
2015.
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[17]
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Ling-Hui Chen, T. Raitio, C. Valentini-Botinhao, Z. Ling, and J. Yamagishi.
A deep generative architecture for postfiltering in statistical
parametric speech synthesis.
Audio, Speech, and Language Processing, IEEE/ACM Transactions
on, 23(11):2003-2014, 2015.
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[18]
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Zhizheng Wu, Cassia Valentini-Botinhao, Oliver Watts, and Simon King.
Deep neural network employing multi-task learning and stacked
bottleneck features for speech synthesis.
In Proceedings of the IEEE International Conference on
Acoustics, Speech, and Signal Processing (ICASSP), 2015.
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[19]
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Cassia Valentini-Botinhao, Junichi Yamagishi, and Simon King.
Intelligibility enhancement of speech in noise.
In Proceedings of the Institute of Acoustics, volume 36 Pt. 2,
pages 96-103, Birmingham, UK, October 2014.
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[20]
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C. Valentini-Botinhao and M. Wester.
Using linguistic predictability and the Lombard effect to increase
the intelligibility of synthetic speech in noise.
In Proc. Interspeech, pages 2063-2067, Singapore, September
2014.
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[21]
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L.-H. Chen, T. Raitio, C. Valentini-Botinhao, J. Yamagishi, and Z.-H. Ling.
DNN-Based Stochastic Postfilter for HMM-Based Speech Synthesis.
In Proc. Interspeech, pages 1954-1958, Singapore, September
2014.
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[22]
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C. Valentini-Botinhao, M. Toman, M. Pucher, D. Schabus, and J. Yamagishi.
Intelligibility Analysis of Fast Synthesized Speech.
In Proc. Interspeech, pages 2922-2926, Singapore, September
2014.
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[23]
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C. Valentini-Botinhao, J. Yamagishi, S. King, and R. Maia.
Intelligibility enhancement of HMM-generated speech in additive
noise by modifying mel cepstral coefficients to increase the glimpse
proportion.
Computer Speech and Language, 28(2):665-686, 2014.
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[24]
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C. Valentini-Botinhao, J. Yamagishi, S. King, and Y. Stylianou.
Combining perceptually-motivated spectral shaping with loudness and
duration modification for intelligibility enhancement of HMM-based synthetic
speech in noise.
In Proc. Interspeech, Lyon, France, August 2013.
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[25]
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M. Cooke, C. Mayo, and C. Valentini-Botinhao.
Intelligibility-enhancing speech modifications: the Hurricane
Challenge.
In Proc. Interspeech, Lyon, France, August 2013.
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[26]
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Cassia Valentini-Botinhao, Mirjam Wester, Junichi Yamagishi, and Simon King.
Using neighbourhood density and selective SNR boosting to increase
the intelligibility of synthetic speech in noise.
In 8th ISCA Workshop on Speech Synthesis, pages 133-138,
Barcelona, Spain, August 2013.
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[27]
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C. Valentini-Botinhao, E. Godoy, Y. Stylianou, B. Sauert, S. King, and
J. Yamagishi.
Improving intelligibility in noise of HMM-generated speech via
noise-dependent and -independent methods.
In Proc. ICASSP, Vancouver, Canada, May 2013.
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[28]
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Cassia Valentini-Botinhao.
Intelligibility enhancement of synthetic speech in noise.
PhD thesis, University of Edinburgh, 2013.
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[29]
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Y. Tang, M. Cooke, and C. Valentini-Botinhao.
A distortion-weighted glimpse-based intelligibility metric for
modified and synthetic speech.
In Proc. SPIN, 2013.
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[30]
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M. Cooke, C. Mayo, C. Valentini-Botinhao, Y. Stylianou, B. Sauert, and Y. Tang.
Evaluating the intelligibility benefit of speech modifications in
known noise conditions.
Speech Communication, 55:572-585, 2013.
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[31]
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C. Valentini-Botinhao, J. Yamagishi, and S. King.
Evaluating speech intelligibility enhancement for HMM-based
synthetic speech in noise.
In Proc. Sapa Workshop, Portland, USA, September 2012.
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[32]
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C. Valentini-Botinhao, S. Degenkolb-Weyers, A. Maier, E. Noeth, U. Eysholdt,
and T. Bocklet.
Automatic detection of sigmatism in children.
In Proc. WOCCI, Portland, USA, September 2012.
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[33]
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C. Valentini-Botinhao, J. Yamagishi, and S. King.
Mel cepstral coefficient modification based on the Glimpse
Proportion measure for improving the intelligibility of HMM-generated
synthetic speech in noise.
In Proc. Interspeech, Portland, USA, September 2012.
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[34]
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C. Valentini-Botinhao, J. Yamagishi, and S. King.
Using an intelligibility measure to create noise robust cepstral
coefficients for HMM-based speech synthesis.
In Proc. LISTA Workshop, Edinburgh, UK, May 2012.
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[35]
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C. Valentini-Botinhao, R. Maia, J. Yamagishi, S. King, and H. Zen.
Cepstral analysis based on the Glimpse proportion measure for
improving the intelligibility of HMM-based synthetic speech in noise.
In Proc. ICASSP, pages 3997-4000, Kyoto, Japan, March 2012.
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[36]
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Cassia Valentini-Botinhao, Junichi Yamagishi, and Simon King.
Can objective measures predict the intelligibility of modified
HMM-based synthetic speech in noise?
In Proc. Interspeech, August 2011.
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[37]
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Cassia Valentini-Botinhao, Junichi Yamagishi, and Simon King.
Evaluation of objective measures for intelligibility prediction of
HMM-based synthetic speech in noise.
In Acoustics, Speech and Signal Processing (ICASSP), 2011 IEEE
International Conference on, pages 5112-5115, May 2011.
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