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NetTalk Project

NetTalk Project. Speech Generation Using a Neural Network. Michael J Euhardy. The Speech Generation Idea. Input: a specific letter whose sound is to be generated Input: three letters on each side of it for a total of seven letters input

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NetTalk Project

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  1. NetTalk Project Speech Generation Using a Neural Network Michael J Euhardy

  2. The Speech Generation Idea • Input: a specific letter whose sound is to be generated • Input: three letters on each side of it for a total of seven letters input • Output: the sound that should be generated based on the input letter and the surrounding letters

  3. The Strategy • 26 possible letters • 7 input position • Map each letter in each position to a unique input 7*26 = 182 total inputs

  4. The Strategy • 57 possible sounds generated • Map to 57 output labels

  5. The Resulting ANN A fully connected single layer perceptron with 182 inputs and 57 outputs

  6. The Findings • The trained neural network performs very well, and the larger the training set and the longer spent training on it, the better it performs • The training can be an extremely long process if a high rate of classification is desired and the training set is large

  7. Problems • Time • Space

  8. Time • You can’t rush training the network. Even using a dual PIII-733 with 512MB, it still took a really long time to train any data of a significant size. And just converting all of the characters in the data file to the matrices necessary to use as inputs and labels took hours.

  9. Space • 20000 words of data with maybe 7 letters on average. That’s a matrix 140000x239 Double precision in Matlab, that’s a lot of memory

  10. Workarounds • Smaller data set, only 1000 words • Lower standards of training, only train to 80% classification

  11. Next Time • C++ • Matlab is way too slow and way too memory intensive • Start Earlier, it’s a long process • Multi-Layer Perceptron

  12. Conclusion • I give up! • I don’t know how Microsoft’s Narrator does it, but I bet it doesn’t do it this way.

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