I have been working on a text-dependent speaker verification project. I have used mel-frequency cepstrum coefficients(MFCC) for the project. As far as I know, MFCC coefficients depend on the vocal characteristics of the speaker, not what he/she says. What features can be extracted from speech that rely both on the speaker's vocal characteristics and what he/she says?

  • $\begingroup$ basically everything that you would reasonably call a speech feature would change depending on what is spoken. Otherwise, it's little use to call it "speech feature", i.e. a distinguishing property within the class of speech. $\endgroup$ Aug 4, 2022 at 13:46
  • $\begingroup$ Related; also see this paper. $\endgroup$ Aug 4, 2022 at 14:15
  • $\begingroup$ @MarcusMüller: You are correct, but I think what M. Fahmin means is a combination of voice features and text-dependent speech features as a function of those voice features (i.e. "the speaker"). I will try and put an answer together. $\endgroup$
    – Max
    Aug 5, 2022 at 8:14

1 Answer 1


MFCC does contain plenty of information about what is being spoken. This is assuming that the time-resolution is on the order of the length of phones (10-100 ms), and that the number of coefficients is still reasonable (13-40). In fact, this feature representation is one of the most common for keyword spotting, automated speech recognition, et.c.


Your Answer

By clicking “Post Your Answer”, you agree to our terms of service and acknowledge you have read our privacy policy.

Not the answer you're looking for? Browse other questions tagged or ask your own question.