TOWARDS SPEAKER AGE ESTIMATION WITH LABEL DISTRIBUTION LEARNING

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, 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.

Automated summary

Existing methods for speaker age estimation face challenges caused by label ambiguity. This paper proposes a method that utilizes label distribution learning to combine age classification and regression approaches, achieving improved performance on a real-world dataset.