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By Damianos Karakos, Haolang Zhou, Puyang Xu, Sanjeev Khudanpur and Andreas G. Andreou


state-of-the-art speech recognition systems use the well-known maximum aposteriori rule ˆW = arg max P (A|W)P (W), W for predicting the uttered word sequence W, given the acoustic information A. The acoustic model is represented by the conditional distribution P (A|W), while the language model is represented by the prior P (W), and the bulk of the research in speech recognition is on training procedures for these two components [1]. Acoustic modeling is usually done in a computationally efficient way, using the maximum likelihood criterion within the parametric family of Gaussian mixtures. The feature space of state-of-the-art systems is typically in the range of hundreds of dimensions—this is the result of concatenating togethe

Year: 2013
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