Neural network performance problem
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I am doing a speech recognition project to classify 4 words; using MFFC.
Since speech divided into frames and features are extracted; often the number of frame is not constant; hence number of features also varies.
1)What is the best best neural network for such problem : Static or Dynamic (Time series)?
I have tried static network; since the input feature vector space has to be constant; i just convert the less feature vector to largest obtainable feature vector by padding zeros to the less feature vector.
2) Is it a good technique for such problem where the number of feature is not constant?
Now some question about the problem i am facing with static network:
1) "GO" and "STOP" words are training well; providing good validation result and test result.
2) "LEFT" and "RIGHT"; among these to words maximum times one of them is training well; in validation and test maximum time they are misclassified between them; I mean LEFT is misclassified as RIGHT and vice-versa.
3)Input matrix dimension is 1157x352 where 352 are the training example. Any comment. Using "nprtool" and hidden neuron is 10.
4)Almost all time validation is stopping the training; Is it a good sign??
5) what is best the training algorithm for such problem?
I want ur precious comment and experience share about such problems.
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