What is the output of MFCCs?

The output after applying MFCC is a matrix having feature vectors extracted from all the frames. In this output matrix the rows represent the corresponding frame numbers and columns represent corresponding feature vector coefficients [1-4].

1. What does MFCC measure?
2. How do you visualize in MFCC?
3. What is MFCC in speech recognition?

What does MFCC measure?

The mel frequency cepstral coefficients (MFCCs) of a signal are a small set of features (usually about 10-20) which concisely describe the overall shape of a spectral envelope. In MIR, it is often used to describe timbre.

How do you visualize in MFCC?

MatPlotLib with Python

Compute MFCC features from an audio signal. Create a figure and a set of subplots. Display the data as an image, i.e., on a 2D regular raster. To display the figure, use show() method.

What is MFCC in speech recognition?

Mel-Frequency Cepstrum Coefficients (MFCC)

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.