Although every <term> natural language system </term> needs a <term> computational lexicon </term> , each <term> system </term> puts different amounts and types of information into its <term> lexicon </term> according to its individual needs .
A <term> probabilistic spectral mapping </term> is estimated independently for each <term> training ( reference ) speaker </term> and the <term> target speaker </term> .
A <term> probabilistic spectral mapping </term> is estimated independently for each <term> training ( reference ) speaker </term> and the <term> target speaker </term> . Each <term> reference model </term> is transformed to the <term> space </term> of the <term> target speaker </term> and combined by <term> averaging </term> .
For <term> pragmatics processing </term> , we describe how the method of <term> abductive inference </term> is inherently robust , in that an interpretation is always possible , so that in the absence of the required <term> world knowledge </term> , performance degrades gracefully . Each of these techniques have been evaluated and the results of the evaluations are presented .
As each new <term> edge </term> is added to the <term> chart </term> , the algorithm checks only the topmost of the <term> edges </term> adjacent to it , rather than all such <term> edges </term> as in conventional treatments .
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