A computationally compact divergence measure for speech processing

The directed divergence, which is a measure based on the discrimination information between two signal classes, is investigated. A simplified expression for computing the directed divergence is derived for comparing two Gaussian autoregressive processes such as those found in speech. This expression alleviates both the computational cost (reduced by two thirds) and the numerical problems encountered in computing the directed divergence. In addition, the simplified expression is compared with the Itakura-Saito distance (which asymptotically approaches the directed divergence). Although the expressions for these two distances closely resemble each other, only moderate correlations between the two were found on a set of actual speech data.<
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