About cookies on this site Our websites require some cookies to function properly (required). In addition, other cookies may be used with your consent to analyze site usage, improve the user experience and for advertising. For more information, please review your options. By visiting our website, you agree to our processing of information as described in IBM’sprivacy statement. To provide a smooth navigation, your cookie preferences will be shared across the IBM web domains listed here.
Publication
ICASSP 2008
Conference paper
Fast Gaussian likelihood computation by maximum probability increase estimation for continuous speech recognition
Abstract
Speech signals are semi-stationary and speech features in neighboring frames are likely to share similar Gaussian distributions. A fast Gaussian computation algorithm is hence proposed to speed up the computation of the JV-best posterior probabilities based on a large set of Gaussian distributions for the task of large vocabulary continuous speech recognition. The maximum probability increase between the current speech frame and a previous reference frame is estimated for all Gaussian distributions in order to reduce explicit computations of posteriors for a large number of Gaussians. The method was applied to the fMPE front-end of IBM's state-of-the-art speech recognizer resulting a decoding speed-up of 40% in probability computation for a loss-less mode and more than 55% in an approximated implementation, respectively. ©2008 IEEE.