SVLDL: Improved Speaker Age Estimation Using Selective Variance Label Distribution Learning
This work addresses the problem of improving age estimation accuracy for speech processing applications, representing an incremental advancement in label distribution learning techniques.
The paper tackled speaker age estimation from speech by proposing a selective variance label distribution learning method to adapt to varying uncertainty in age distributions, achieving state-of-the-art performance on NIST SRE08-10 and a real-world dataset.
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.