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