Publication Type : Book Chapter
Publisher : De Gruyter
Source : Communication and Power Engineering
Url : https://doi.org/10.1515/9783110469608-008
Campus : Coimbatore
School : School of Computing
Department : Computer Science and Engineering
Year : 2016
Abstract :
Gender classification is one of the initial steps in any of the speaker recognition system. This paper deals with the comparative analysis of two of the important gender classification methods, namely Cepstral analysis and Short time Autocorrelation Method, experimented on both natural and synthetic voices. From the experimental results, it is concluded that autocorrelation method performs better gender classification.
Cite this Research Publication : Shilpa Gopal, S. Padmavathi, Comparative Analysis of Cepstral analysis and Autocorrelation Method for Gender Classification, Communication and Power Engineering, De Gruyter, 2016, https://doi.org/10.1515/9783110469608-008