  In this paper, a novel multitaper modified group delay function-based
representation for speech signals is proposed. With a set of phoneme-based
experiments, it is shown that the proposed method performs better that an
existing multitaper magnitude (MT-MAG) estimation technique, in terms of
variance and MSE, both in spectral- and cepstral-domains. In particular, the
performance of MT-MOGDF is found to be the best with the Thomson tapers.
Additionally, the utility of the MT-MOGDF technique is highlighted in a speaker
recognition experimental setup, where an improvement of around $20\%$ compared
to the next-best technique is obtained. Moreover, the computational
requirements of the proposed technique is comparable to that of MT-MAG. The
proposed feature can be used in for many speech-related applications; in
particular, it is best suited among those that require information of speaker
and speech.
