Speaker identification using Gaussian mixture models based on multi-space probability distribution

Presents an approach to modeling speech spectra and pitch for text-independent speaker identification using Gaussian mixture models based on multi-space probability distribution (MSD-GMM). The MSD-GMM allows us to model continuous pitch values for voiced frames and discrete symbols representing unvoiced frames in a unified framework. Spectral and pitch features are jointly modeled by a two-stream MSD-GMM. We derive maximum likelihood estimation formulae for the MSD-GMM parameters, and the MSD-GMM speaker models are evaluated for text-independent speaker identification tasks. Experimental results, show that the MSD-GMM can efficiently model spectral and pitch features of each speaker and outperforms conventional speaker models