DeepPredSpeech: computational models of predictive speech coding based on deep learning

This dataset contains all data, source code, pre-trained computational predictive models and experimental results related to:   Hueber T., Tatulli E., Girin L., Schwatz, J-L "How predictive can be predictions in the neurocognitive processing of auditory and audiovisual speech? A deep learning study." (biorXiv preprint https://doi.org/10.1101/471581).  Raw data are extracted from the publicly available database NTCD-TIMIT (10.5281/zenodo.260228).  Audio recordings are available in the audio_clean/ directory Post-processed lip image sequences are available in the lips_roi/ directory (67x67 pixels, 8bits, obtained by lossless inverse DCT-2D transform from the DCT feature available in the original repository of NTCD-TIMIT) Phonetic segmentation (extracted from NTCD-TIMIT original zenodo repository) is available in the HTK MLF file volunteer_labelfiles.mlf Audio features (MFCC-spectrogram and log-spectrogram) are available in the mfcc_16k/ and fft_16k/ directories.  Models (audio-only, video-only and audiovisual, based on deep feed-forward neural networks and/or convolutional neural network, in .h5 format, trained with Keras 2.0 toolkit) and data normalization parameters (in .dat scikit-learn format) are available in models_mfcc/ and models_logspectro/ directories Predicted and target (ground truth) MFCC-spectro (resp. log-spectro) for the test databases (1909 sentences), and for the different values of (\tau_p) or (\tau_f) are available in pred_testdb_mfccspectro/ (resp. pred_testdb_logspectro/) directory Source code for extracting audio features, training and evaluating the models is available on GitHub https://github.com/thueber/DeepPredSpeech/ All directories have been zipped before upload. Feel free to contact me for more details. Thomas Hueber, Ph. D., CNRS research fellow, GIPSA-lab, Grenoble, France, thomas.hueber@gipsa-lab.fr