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I think that this approach should work rather well (I did very simillar project few years ago). Things you might consider:

1. You probably might want to use pre-emphasis filter on a signal.
2. In addition to MFCC features you can also include $\Delta$ and $\Delta\Delta$. Some theory can be found for example here: click!
3. Comparing against more templates in my case largely improved recognition rate. I think that the easiest way do so is by using k-NN algorithm on distances returned from DWT.

Good luck!

I think that this approach should work rather well (I did very similar project few years ago). Things you might consider:

1. You probably might want to use pre-emphasis filter on a signal.
2. In addition to MFCC features you can also include $\Delta$ and $\Delta\Delta$ (first and second derivatives - simple differences).
3. Comparing against more templates in my case largely improved recognition rate. I think that the easiest way do so is by using k-NN algorithm on distances returned from DWT.

Good luck!

I think that this approach should work rather well (I did very simillar project few years ago). Things you might consider:

1. You probably might want to use pre-emphasis filter on a signal.
2. In addition to MFCC features you can also include $\Delta$ and $\Delta\Delta$. Some theory can be found for example here
3. Comparing against more templates in my case largely improved recognition rate. I think that the easiest way do so is by using k-NN algorithm on distances returned from DWT.

Good luck!

I think that this approach should work rather well (I did very simillar project few years ago). Things you might consider:

1. You probably might want to use pre-emphasis filter on a signal.
2. In addition to MFCC features you can also include $\Delta$ and $\Delta\Delta$. Some theory can be found for example here: click!
3. Comparing against more templates in my case largely improved recognition rate. I think that the easiest way do so is by using k-NN algorithm on distances returned from DWT.

Good luck!

I think that this approach should work rather well (I did very simillar project few years ago). Things you might consider:

1. You probably might want to use pre-emphasis filter on a signal.
2. In addition to MFCC features you can also include $\Delta$ and $\Delta\Delta$. Some theory can be found for example here: click!
3. Comparing against more templates in my case largely improved recognition rate. I think that the easiest way do so is by using k-NN algorithm on distances returned from DWT.

Good luck!

I think that this approach should work rather well (I did very simillar project few years ago). Things you might consider:

1. You probably might want to use pre-emphasis filter on a signal.
2. In addition to MFCC features you can also include $\Delta$ and $\Delta\Delta$. Some theory can be found for example here
3. Comparing against more templates in my case largely improved recognition rate. I think that the easiest way do so is by using k-NN algorithm on distances returned from DWT.

Good luck!