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Estimating age from a single speech is a classic and challenging topic.Although Label Distribution Learning (LDL) can represent adjacentindistinguishable ages well, the uncertainty of the age estimate for eachutterance varies from person to person, i.e., the variance of the agedistribution is different. To address this issue, we propose selective variancelabel distribution learning (SVLDL) method to adapt the variance of differentage distributions. Furthermore, the model uses WavLM as the speech featureextractor and adds the auxiliary task of gender recognition to further improvethe performance. Two tricks are applied on the loss function to enhance therobustness of the age estimation and improve the quality of the fitted agedistribution. Extensive experiments show that the model achievesstate-of-the-art performance on all aspects of the NIST SRE08-10 and areal-world datasets.

SVLDL: Improved Speaker Age Estimation Using Selective Variance Label Distribution Learning