Abstract
Mel-frequency cepstrum coefficient (MFCC) is a widely used feature vector in speech signal precessing. Its feature extraction procedure can be seen as a mapping function which transfers the input speech signals to output MFCC feature vectors. However, this function is too complex to analyze and even a simple approximation is not easy to obtain. This paper studies the effects of each MFCC feature extraction step and obtains the relation between the input signal-to-noise ratio (SNR) and the output perturbation bound of MFCC feature vectors. Experimental results show that the obtained bounds are ”tight” and nearly full covered. This analysis method may help us to find new clue of MFCC and may has potential applications in speech recognition.
Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
