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Experiments in deep learning for speech synthesis | Jessica Thompson's journal for IFT6266 – Representation Learning at Université de Montréal
Experiments in deep learning for speech synthesis. Jessica Thompson's journal for IFT6266 – Representation Learning at Université de Montréal. Skip to primary content. Skip to secondary content. January 24, 2014. This blog will document my progress on the final project for Dr. Yoshua Bengio’s course on Representation Learning. The task for the project is speech synthesis. April 30, 2014. Vincent published a blog post on h ow to use the Pylearn2 TIMIT class with multiple sources, specifically c. Monitor i...
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Speech synthesis experiments | Smile! You’re at the best WordPress.com site ever
You’re at the best WordPress.com site ever. The plan, at this point, is to use a gammatone dictionary and sparse coding to train more efficiently on the TIMIT dataset. Gammatone functions are filters applied on frequencies. The goal is to keep frequencies processed by our ears. Joao explains this greatly in his blog. The idea is to use this sparse-coded version as input of a spike and slab RBM segmented by frames of 160 samples and with the previous, current and next phones. I kept the parameters use...
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IFT6266 project | Experiments in speech synthesis with neural networks by Pierre-Luc Vaudry
Experiments in speech synthesis with neural networks by Pierre-Luc Vaudry. Predicting the standard deviation. Hubert Banville successfully implemented the kind of architecture I also had in mind. In this experiment, I wanted to see if having this standard deviation predicted by the neural network could improve the results. I based myself on Hubert’s code, which is described in his last post. To this loss function, the L1 and L2 norm penalties are also added as regulators, as in Hubert’s experiments...
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