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         author = {Pronobis, Marianna and Magimai.-Doss, Mathew},
       projects = {Idiap, AMIDA},
          month = {11},
          title = {Analysis of F0 and Cepstral Features for Robust Automatic Gender Recognition},
           type = {Idiap-RR},
         number = {Idiap-RR-30-2009},
           year = {2009},
    institution = {Idiap},
       abstract = {In this paper, we analyze applicability of F0 and cepstral features,
namely LPCCs, MFCCs, PLPs for robust Automatic Gender
Recognition (AGR). Through gender recognition studies
on BANCA corpus comprising datasets of varying complexity,
we show that use of voiced speech frames and modelling
of higher spectral detail (i.e. using higher order cepstral coefficients)
along with the use of dynamic features improve the
robustness of the system towards mismatched training and test
conditions. Moreover, our study shows that for matched clean
training and test conditions and for multi-condition training, the
AGR system is less sensitive to the order of cepstral coefficients
and the use of dynamic features gives little-to-no gain. F0 and
cepstral features perform equally well under clean conditions,
however under noisy conditions cepstral features yield robust
system compared to F0-based system.},
            pdf = {https://publications.idiap.ch/attachments/reports/2009/Pronobis_Idiap-RR-30-2009.pdf}