Tessellated Linear Model for Age Prediction from Voice
This addresses the challenge of voice-based age prediction for biometric applications where data is scarce, offering a method that balances simplicity and non-linear capacity, though it is incremental as it builds on piecewise linear techniques.
The paper tackles the problem of age prediction from voice with limited labeled data by proposing the Tessellated Linear Model (TLM), a piecewise linear approach that tessellates the feature space and fits linear models per region, which outperformed state-of-the-art deep learning models on the TIMIT dataset.
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.