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This paper describes an approach where posterior-based features are applied in those recognition tasks where the amount of training data is insufficient to obtain a reliable estimate of the speech variability. A template matching approach is considered in this paper where posterior features are obtained from a MLP trained on an auxiliary database. Thus, the speech variability present in the features is reduced by applying the speech knowledge captured on the auxiliary database. When compared to state-of-the-art systems, this approach outperforms acoustic-based techniques and obtains comparable results to grapheme-based approaches. Moreover, the proposed method can be directly combined with other posterior-based HMM systems. This combination successfully exploits the complementarity between templates and parametric models.
Petr Motlicek, Hynek Hermansky, Sriram Ganapathy, Amrutha Prasad
Petr Motlicek, Juan Pablo Zuluaga Gomez, Amrutha Prasad
Petr Motlicek, Hynek Hermansky, Sriram Ganapathy, Amrutha Prasad