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Author Title Type [ Year(Asc)]
P. Karanasou, Wang, Y., Gales, M., and Woodland, P., Adaptation of Deep Neural Network Acoustic Models Using Factorised I-vectors, in Proceedings of Interspeech’14, 2014.
I. Casanueva, Christensen, H., Hain, T., and Green, P., "Adaptive speech recognition and dialogue management for users with speech disorders, in Proceedings of Interspeech'14, 2014.
H. Christensen, Casanueva, I., Cunningham, S., Green, P., and Hain, T., Automatic Selection of Speakers for Improved Acoustic Modelling : Recognition of Disordered Speech with Sparse Data, in Spoken Language Technology Workshop, SLT'14, Lake Tahoe, 2014.
P. Swietojanski, Ghoshal, A., and Renals, S., Convolutional Neural Networks for Distant Speech Recognition, Signal Processing Letters, IEEE, vol. 21, pp. 1120-1124, 2014.
L. Lu, Ghoshal, A., and Renals, S., Cross-lingual subspace Gaussian mixture model for low-resource speech recognition, IEEE Transactions on Audio, Speech and Language Processing, 2014.
R. Dall, Wester, M., and Corley, M., The Effect of Filled Pauses and Speaking Rate on Speech Comprehension in Natural, Vocoded and Synthetic Speech, in Proceedings of Interspeech, 2014.
R. Dall, Tomalin, M., Wester, M., Byrne, W., and King, S., Investigating Automatic & Human Filled Pause Insertion for Speech Synthesis, in Proceedings of Interspeech, 2014.
P. Swietojanski and Renals, S., Learning Hidden Unit Contributions for Unsupervised Speaker Adaptation of Neural Network Acoustic Models, in Proc. IEEE Workshop on Spoken Language Technology, Lake Tahoe, USA, 2014.
P. Lanchantin, Gales, M. J. F., King, S., and Yamagishi, J., Multiple-Average-Voice-based Speech Synthesis, in Proc. ICASSP, 2014.
S. Renals and Swietojanski, P., Neural Networks for Distant Speech Recognition, in The 4th Joint Workshop on Hands-free Speech Communication and Microphone Arrays (HSCMA), 2014.
X. Liu, Gales, M., and Woodland, P., PARAPHRASTIC NEURAL NETWORK LANGUAGE MODELS, in IEEE ICASSP2014, Florence, Italy, 2014.
L. Lu and Renals, S., Probabilistic Linear Discriminant Analysis for Acoustic Modelling, IEEE Signal Processing Letters, vol. 21, pp. 702-706, 2014.
C. Zhang and Woodland, P. C., Standalone training of context-dependent deep neural network acoustic models, in IEEE ICASSP 2014, Florence, Italy, 2014.
O. Saz and Hain, T., Using Contextual Information in Joint Factor Eigenspace MLLR for Speech Recognition in Diverse Scenarios, in Proceedings of the 2014 ICASSP, Florence, Italy., 2014.
C. Valentini-Botinhao and Wester, M., Using linguistic predictability and the Lombard effect to increase the intelligibility of synthetic speech in noise, in Proceedings of Interspeech, 2014.
Y. Liu, Zhang, P., and Hain, T., Using neural network front-ends on far field multiple microphones based speech recognition, in ICASSP2014 - Speech and Language Processing (ICASSP2014 - SLTC), Florence, Italy, 2014.
L. Lu, Ghoshal, A., and Renals, S., Acoustic Data-driven Pronunciation Lexicon for Large Vocabulary Speech Recognition, in Proc. IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU), 2013.
O. Saz and Hain, T., Asynchronous factorisation of speaker and background with feature transforms in speech recognition, in Proceedings of Interspeech 2013, Lyon, France, 2013.
P. Lanchantin, Bell, P. - J., Gales, M. - J. - F., Hain, T., Liu, X., Long, Y., Quinnell, J., Renals, S., Saz, O., Seigel, M. - S., Swietojanski, P., and Woodland, P. - C., Automatic Transcription of Multi-genre Media Archives, in Proceedings of SLAM Workshop, Marseille, France, 2013.
M. Shannon, Zen, H., and Byrne, W., Autoregressive models for statistical parametric speech synthesis, IEEE Trans. Audio Speech Language Process., vol. 21, pp. 587–597, 2013.
H. Lu, King, S., and Watts, O., Combining a Vector Space Representation of Linguistic Context with a Deep Neural Network for Text-To-Speech Synthesis, in 8th ISCA Workshop on Speech Synthesis, Barcelona, Spain, 2013, pp. 281–285.
H. Christensen, Aniol, M. B., Bell, P., Green, P., Hain, T., King, S., and Swietojanski, P., Combining in-domain and out-of-domain speech data for automatic recognition of disordered speech, in Interspeech'13, 2013.
X. Liu, Gales, M., and Woodland, P., Cross-domain Paraphrasing For Improving Language Modelling Using Out-of-domain Data, in ISCA Interspeech2013, Lyon, France, 2013.
J. Driesen, Bell, P., and Renals, S., Description of the UEDIN system for German ASR, in Proc. IWSLT, 2013.