# Comparing MFCC Features ,What do they represent?

I know that MFCC features are the spectral envelope of the input signal but I can't understand what do they mean and what do they represent . and if I have two people saying the same word how can I compare the resulting features.

For example I have Mel Frequency Cepstral Coefficient for the word "please": first person:

second person:

I see these signals seem different but thay are representing the same word which is 'please' .

I've done these steps to extract the features:

• framing with overlapping percentage equals to 50%.
• Windowing : hamming window was used
• perform fft(signal);
• take the squared magnitude of the ff(signal);
• apply mel filter bank :number of filters = 26;
• take the logarithm of squared sum for each filter
• perform dct .
• Well, definitely you must not compare them by an "eye inspection", unless you can imagine 12+ dimensions. Easiest way is to calculate the distance. Obviously due to the differences in variance, you can't use simple Euclidean distance. For that purpose you should use the Mahalanobis distance since it takes the difference of variance into account (first it should be calculated across the dataset). Another thing is the time warping. In that case use the DTW.
– jojeck
Dec 1, 2015 at 19:20
• What do you exactly mean? It should be obvious with an example if you know about what MFCC-calculation does that the same words spoken by different persons (or even by the same person repeatedly) will differ for example in their pitch (or pronounciation) and therefore frequency components per time. Just think about saying "please" in a friendly and in an angry way. In the example you posted the MFCCs seem to similar to make intuitive assumptions about why they differ. Dec 1, 2015 at 23:03