Extracting speech features
We learnt how to convert a time domain signal into the frequency domain. Frequency domain features are used extensively in all the speech recognition systems. The concept we discussed earlier is an introduction to the idea, but real world frequency domain features are a bit more complex. Once we convert a signal into the frequency domain, we need to ensure that it's usable in the form of a feature vector. This is where the concept of Mel Frequency Cepstral Coefficients (MFCCs) becomes relevant. MFCC is a tool that's used to extract frequency domain features from a given audio signal.
In order to extract the frequency features from an audio signal, MFCC first extracts the power spectrum. It then uses filter banks and a discrete cosine transform (DCT) to extract the features. If you are interested in exploring MFCC further, you can check out this link: http://practicalcryptography.com/miscellaneous/machine-learning/guide-mel-frequency-cepstral...
