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A Computationally Compact Divergence Measure for Speech Processing
December 1991 (vol. 13 no. 12)
pp. 1255-1260

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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Index Terms:
computationally compact divergence measure; speech processing; discrimination information; signal classes; Gaussian autoregressive processes; Itakura-Saito distance; correlation methods; matrix algebra; speech analysis and processing
Citation:
B.A. Carlson, M.A. Clements, "A Computationally Compact Divergence Measure for Speech Processing," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 13, no. 12, pp. 1255-1260, Dec. 1991, doi:10.1109/34.106999
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