7th International Conference on Spoken Language Processing
September 16-20, 2002
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 strategies 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.
Bibliographic reference. Lee, Raymond H. / Choi, Eric H. C. (2002): "A Gaussian selection method for multi-mixture HMM based continuous speech recognition", In ICSLP-2002, 381-384.