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Perspectives on predictive power of multimodal deep learning: surprises and future directions.

, , , and . The Handbook of Multimodal-Multisensor Interfaces, Volume 2 (2), Association for Computing Machinery, (2018)

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Analysis of the correlation structure for a neural predictive model with application to speech recognition., , and . Neural Networks, 7 (2): 331-339 (1994)A deep architecture with bilinear modeling of hidden representations: Applications to phonetic recognition., , and . ICASSP, page 4805-4808. IEEE, (2012)RoMD: Robust device-free motion detection usin PHY layer information., , , , and . SECON, page 154-156. IEEE, (2015)A deep convolutional neural network using heterogeneous pooling for trading acoustic invariance with phonetic confusion., , and . ICASSP, page 6669-6673. IEEE, (2013)Efficient and Robust Language Modeling in an Automatic Children's Reading Tutor System., , , and . ICASSP (4), page 193-196. IEEE, (2007)New types of deep neural network learning for speech recognition and related applications: an overview., , and . ICASSP, page 8599-8603. IEEE, (2013)Variational inference and learning for segmental switching state space models of hidden speech dynamics., , and . ICASSP (1), page 872-875. IEEE, (2003)A Bayesian approach to speech feature enhancement using the dynamic cepstral prior., , and . ICASSP, page 829-832. IEEE, (2002)Towards deeper understanding: Deep convex networks for semantic utterance classification., , , and . ICASSP, page 5045-5048. IEEE, (2012)Hidden Markov model representation of quantized articulatory features for speech recognition., and . Comput. Speech Lang., 7 (3): 265-282 (1993)