This paper concerns improving Gaussian selection for reducing output probability computation. We investigate the use of principal component analysis (PCA) to generate questions for a decision tree which is then used to cluster a set of Gaussians for selection purpose. By dividing a feature vector into several subspaces and generating a decision tree for each subspace, we are able to generate a smaller shortlist and hence reduce computation further. Moreover we investigate different voting

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... gies to combine the shortlists selected from individual decision trees. Experiments on a Mandarin Chinese base syllable recognition task have revealed that our proposed method virtually does not degrade recognition accuracy, even though there is more than 50% reduction in output probability computation.