Speaker identification using Gaussian mixture models based on multi-space probability distribution
نویسندگان
چکیده
This paper presents a new 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 (ML) 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.
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