question, performance not match with the paper and feature generation

[arm-software]

question, performance not match with the paper and feature generation

[navsuda]

Hi @zhangjinhong17

1. Such difference is expected because of differences in the weight initialization.
2. The Table 7 accuracies are obtained from the checkpoint with highest accuracy on validation set (i.e. the last saved checkpoint). Use test.py to test the accuracy on your checkpoints.
3. We did not compare LFBE vs. MFCC and we did not any observe higher performance (i.e. accuracy) using more MFCC features. It might be a function of the number and type of output words you are classifying, for example, you may need a higher resolution (i.e. more features) to differentiate "light" vs. "flight".
4. If you were asking about batch normalization to normalize the features across different inputs, it seems to work fine with this dataset. It would be interesting to see how well the batch norm parameters generalize to another dataset.
5. MFCC computation is a part of Tensorflow, where a delta of 1e-12 is used. Check here for more details: https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/kernels/mfcc.cc#L60.
6. That's a good point, as 40ms will give 640 samples and you would have to do pad it to 1024 to perform FFT. However, typically, the total number of operations in neural network is much higher than the number of computations in FFT and hence it will not matter. It might matter, though, when the neural network is squeezed down to <1MOps per inference. In our case, window size of 40ms was the result of the initial hyperparameter search.
7. Training time is a function of network size and from what we have seen, for small networks you should get a good enough accuracy within the first hour and only incremental accuracy improvement (~1-2%) after that.

[meixitu]

Hi @navsuda ,

1. You are right. I run the same code twice, and the result is a little different. 

Thanks for your other reply.

Thanks
Jinhong