EMPLOYMENT OF SUBSPACE GAUSSIAN MIXTURE MODELS IN SPEAKER RECOGNITION

This paper presents Subspace Gaussian Mixture Model (SGMM)approach employed as a probabilistic generative model to estimatespeaker vector representations to be subsequently used in the speakerverification task. SGMMs have already been shown to significantlyoutperform traditional HMM/GMMs in Automatic Speech Recognition(ASR) applications. An extension to the basic SGMM frameworkallows to robustly estimate low-dimensional speaker vectorsand exploit them for speaker adaptation. We propose a speaker verificationframework based on low-dimensional speaker vectors estimatedusing SGMMs, trained in ASR manner using manual transcriptions.To test the robustness of the system, we evaluate theproposed approach with respect to the state-of-the-art i-vector extractoron the NIST SRE 2010 evaluation set and on four differentlength-utterance conditions: 3sec-10sec, 10 sec-30 sec, 30 sec-60 secand full (untruncated) utterances. Experimental results reveal thatwhile i-vector system performs better on truncated 3sec to 10sec and10 sec to 30 sec utterances, noticeable improvements are observedwith SGMMs especially on full length-utterance durations. Eventually,the proposed SGMM approach exhibits complementary propertiesand can thus be efficiently fused with i-vector based speakerverification system.