Speech based gender identification using empirical mode decomposition (EMD)
AuthorChowdhury, Mahpara Hyder
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Traditionally for feature extraction, decomposition techniques such as Fourier decomposition are used to capture signals. But those methods have some margins like – it only works for linear and stationary data. On the other hand, in real world, we found data that are in non-linear and non-stationary. EMD or Empirical Mode Decomposition technique is a new approach introduced by Huang et al (1998) that can take any complicated signal and decomposed it to IMF. It extracts the amplitude and frequency information of a signal at a particular time. It is robust for non-linear and non-stationary signal processing. In this paper, I am using EMD as a new approach for gender identification based on speech signal. Gender identification based on the voice of a speaker consists of detecting if a speech signal is given by a male or female. Detecting the gender of a speaker has several applications.