Analysis of F0 and Cepstral Features for Robust Automatic Gender Recognition

@inproceedings{Pronobis2009AnalysisOF,
  title={Analysis of F0 and Cepstral Features for Robust Automatic Gender Recognition},
  author={Marianna Pronobis and Mathew Magimai.-Doss},
  year={2009}
}
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… CONTINUE READING

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