GMM-Based Speaker Gender and Age Classification After Voice Conversion

This paper describes an experiment using the Gaussian mixture models (GMM) for classification of the speaker gender/age and for evaluation of the achieved success in the voice conversion process. The main motivation of the work was to test whether this type of the classifier can be utilized as an alternative approach instead of the conventional listening test in the area of speech evaluation. The proposed two-level GMM classifier was first verified for detection of four age categories (child, young, adult, senior) as well as discrimination of gender for all but children's voices in Czech and Slovak languages. Then the classifier was applied for gender/age determination of the basic adult male/female original speech together with its conversion. The obtained resulting classification accuracy confirms usability of the proposed evaluation method and effectiveness of the performed voice conversions.