用分块线性模型提升语音年龄预测，小数据下表现优于深度学习。
Tessellated Linear Model for Age Prediction from Voice
- 将特征空间切分成凸区域，每个区域拟合线性模型。
- 在TIMIT数据集上，平均绝对误差达9.2岁，优于现有深度模型。
- 适合数据量小、需可解释性的语音生物特征任务。
语音生物识别任务如年龄估计需要建模语音特征与生物变量之间的复杂关系。尽管深度学习模型能处理这种复杂性，但通常需要大量标注数据才能表现良好，而语音年龄预测等任务中此类数据往往稀缺。相比之下，简单模型如线性回归虽可用小数据，却难以捕捉数据中的非线性模式。本文提出分块线性模型（Tessellated Linear Model, TLM），一种结合线性模型简洁性与非线性函数能力的分段线性方法。TLM将特征空间划分为凸区域，并在每个区域内拟合线性模型，通过分层贪心分割优化划分方式和模型参数。在TIMIT数据集上的语音年龄预测任务中，TLM性能超越当前最优深度学习模型。
原文摘要 · Abstract (English)
Voice biometric tasks, such as age estimation require modeling the often complex relationship between voice features and the biometric variable. While deep learning models can handle such complexity, they typically require large amounts of accurately labeled data to perform well. Such data are often scarce for biometric tasks such as voice-based age prediction. On the other hand, simpler models like linear regression can work with smaller datasets but often fail to generalize to the underlying non-linear patterns present in the data. In this paper we propose the Tessellated Linear Model (TLM), a piecewise linear approach that combines the simplicity of linear models with the capacity of non-linear functions. TLM tessellates the feature space into convex regions and fits a linear model within each region. We optimize the tessellation and the linear models using a hierarchical greedy partitioning. We evaluated TLM on the TIMIT dataset on the task of age prediction from voice, where it outperformed state-of-the-art deep learning models.
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