TY - CONF AB - In this contribution we derive the Maximum A-Posteriori (MAP) estimates of the parameters of a Gaussian Mixture Model (GMM) in the presence of noisy observations. We assume the distortion to be white Gaussian noise of known mean and variance. An approximate conjugate prior of the GMM parameters is derived allowing for a computationally efficient implementation in a sequential estimation framework. Simulations on artificially generated data demonstrate the superiority of the proposed method compared to the Maximum Likelihood technique and to the ordinary MAP approach, whose estimates are corrected by the known statistics of the distortion in a straightforward manner. AU - Chinaev, Aleksej AU - Haeb-Umbach, Reinhold ID - 11740 KW - Gaussian noise KW - maximum likelihood estimation KW - parameter estimation KW - GMM parameter KW - Gaussian mixture model KW - MAP estimation KW - Map-based estimation KW - maximum a-posteriori estimation KW - maximum likelihood technique KW - noisy observation KW - sequential estimation framework KW - white Gaussian noise KW - Additive noise KW - Gaussian mixture model KW - Maximum likelihood estimation KW - Noise measurement KW - Gaussian mixture model KW - Maximum a posteriori estimation KW - Maximum likelihood estimation SN - 1520-6149 T2 - 38th International Conference on Acoustics, Speech and Signal Processing (ICASSP 2013) TI - MAP-based Estimation of the Parameters of a Gaussian Mixture Model in the Presence of Noisy Observations ER -