The paper reviews two basic time--frequency distributions,
spectrogram and cone--shaped kernel distribution applied to speech
signals. We are proposing a new modified method of speech features
extracting based on mel--frequency cepstral coefficients with use
of the cone--shaped kernel distribution. We are additionally
exploring several estimates of the time derivatives approximated
by regression coefficients and coefficients determined by
trigonometric functions. Analyzes and tests are performed for
different sets of speech features obtained from spectrogram and
cone--shaped kernel distribution using speech recognition system
based on hidden Markov acoustic models. Our main goal has been to
incorporate different time--frequency distributions into a speech
features extraction process and potentially find an alternative
way of deriving speech features based on these distributions.