  Existing methods for speaker age estimation usually treat it as a multi-class
classification or a regression problem. However, precise age identification
remains a challenge due to label ambiguity, \emph{i.e.}, utterances from
adjacent age of the same person are often indistinguishable. To address this,
we utilize the ambiguous information among the age labels, convert each age
label into a discrete label distribution and leverage the label distribution
learning (LDL) method to fit the data. For each audio data sample, our method
produces a age distribution of its speaker, and on top of the distribution we
also perform two other tasks: age prediction and age uncertainty minimization.
Therefore, our method naturally combines the age classification and regression
approaches, which enhances the robustness of our method. We conduct experiments
on the public NIST SRE08-10 dataset and a real-world dataset, which exhibit
that our method outperforms baseline methods by a relatively large margin,
yielding a 10\% reduction in terms of mean absolute error (MAE) on a real-world
dataset.
