A number of paralinguistic problems are often dealt with in isolation, such as emotion, health state or personality. However, there are also good examples of mutual benefit, mostly incorporating speaker gender knowledge. In this paper we deal with the question how further paralinguistic information, such as speaker age, height, or race can provide beneficial information when their ground truth knowledge is provided within single-task speaker classification. Tests with openSMILE's 1.5 k Paralinguistic Challenge Feature set on the TIMIT corpus of 630 speakers reveal significant boost in accuracy or cross-correlation|depending on the representation form of the problem at hand.
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