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About this paper
Shijing Si, Jianzong Wang, Junqing Peng, Jing Xiao
A framework leveraging Label Distribution Learning (LDL) is proposed for speaker age estimation, modeling age as a probability distribution to account for its continuous and ambiguous nature. The method, using a hybrid loss with variance regularization, achieved over a 10% reduction in Mean Absolute Error (MAE) compared to baseline methods on a proprietary real-world dataset.
Key insights
- Representing age as a probability distribution (via LDL) effectively models both the ordinal relationship and the inherent ambiguity of speaker age, leading to more robust learning.
- A multi-component loss function, integrating distribution matching, direct regression, and variance regularization, is crucial for achieving high accuracy and confidence in speaker age estimation.
- The proposed framework is feature-agnostic, demonstrating improved performance across various state-of-the-art acoustic feature extractors (i-vector, x-vector, ResNet-18).
Summary by alphaXiv.
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