On talker-independent word recognition in continuous speech

Results are presented from an experiment on talker-independent word recognition in which three experimental parameters were manipulated. Those parameters were 1) whether the word model had a mean vector and a variance vector, or a mean vector and a covariance matrix, at each time frame; 2) whether a single estimate was made of the word model or whether there was one re-estimate; and 3) whether word recognition was based on a Pass 1 distance score or on a novel Pass 2 log odds score. The results were 1) a multivariate word model did not do better than a word model based on a sum of univariate distributions; 2) a re-estimated word model did better than a word model based on initial estimates; and 3) the Pass 2 log odds score did better than the Pass 1 distance score.