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The subspace Gaussian mixture model (SGMM) has been recently proposed as an acoustic modeling technique suitable for configuring multilingual speech recognition systems. It is attractive for this purpose since its parametrization allows its "shared" model parameters to be trained with data from multiple languages  . In this work, we report on the results of an experimental study carried out with the goal of improving native Spanish language speech recognition performance using an existingdoi:10.1109/icassp.2012.6289016 dblp:conf/icassp/MohanGR12 fatcat:cwpjtve2cjd2hfwfvxoi7qiuhq