Issue with the MFCC and GMM for audio recognition

1. Why is MFCC important in speech recognition?
2. How MFCC features are extracted for the speech recognition?
3. Why are MFCC so popular?
4. What is the advantages of MFCC?

Why is MFCC important in speech recognition?

MFCC are popular features extracted from speech signals for use in recognition tasks. In the source-filter model of speech, MFCC are understood to represent the filter (vocal tract). The frequency response of the vocal tract is relatively smooth, whereas the source of voiced speech can be modeled as an impulse train.

How MFCC features are extracted for the speech recognition?

The MFCC feature extraction technique basically includes windowing the signal, applying the DFT, taking the log of the magnitude, and then warping the frequencies on a Mel scale, followed by applying the inverse DCT.

Why are MFCC so popular?

The MFCC technique is a most popular, has a huge achievement and extensively used in the speaker and speech recognition systems [35, 36]. It is based on a logarithmic scale and is able to estimates human auditory response in a better way than the other cepstral feature extraction techniques [37,38]. ...

What is the advantages of MFCC?

The advantage of MFCC is that it is good in error reduction and able to produce a robust feature when the signal is affected by noise. SVD/PCA technique is used to extract the important features out of the B-Distribution representation.