Speech Age Estimation Using Label Distribution Loss
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Solution Overview
Problem
Conventional age estimation systems for speech-based biometrics face challenges due to limited datasets and the inability to incorporate ambiguities or ordinal rankings, leading to inefficiencies and inaccuracies in age estimation.
Innovation Solution
A machine-learning architecture is implemented with a speaker embedding extraction engine and an age estimation engine, utilizing label distribution modeling and loss functions to improve age estimation performance and generalization, accommodating uncertainty and ambiguity in age estimation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional regression or classification techniques are used for age estimation, then the system is simple to implement, but it cannot incorporate ambiguities or ordinal rankings leading to inaccuracies
Solution Approach 1:
The patent transforms the age estimation problem from traditional regression/classification by changing the output parameter from a single age value to a probability distribution over age ranges. This is achieved by modifying the neural network output layer to produce multiple probability values representing different age bins, and using a specialized loss function that incorporates ordinal relationships between age ranges. This parameter change enables the model to capture ambiguities while maintaining reasonable complexity.
Solution Approach 2:
The patent adds a dimensional transformation by converting scalar age predictions into multi-dimensional probability distributions. Instead of predicting a single age value, the model outputs a vector of probabilities across multiple age ranges, adding a distributional dimension to the prediction. This dimensional expansion allows the system to represent uncertainty and ordinal relationships without excessive complexity.
2Productivity
If limited datasets are used for training, then data collection is efficient, but the model generalization performance deteriorates
Solution Approach 1:
The patent changes the training objective parameter from minimizing simple prediction error to minimizing a specialized loss function that incorporates ordinal constraints and distributional information. This parameter change in the optimization objective enables better utilization of limited training data by imposing structurally sound constraints that improve generalization without requiring additional data collection.
Solution Approach 2:
The patent implements feedback mechanisms through the specialized loss function that provides structured training signals. The loss function computes gradients that incorporate ordinal relationships between age ranges, giving the model feedback about the relative ordering of predictions. This feedback structure helps the model learn from limited data more effectively by leveraging the inherent ordinal nature of age information.
3Reliability
If probability distribution modeling is implemented, then uncertainty and ambiguity are accommodated, but computational complexity increases
Solution Approach 1:
The patent applies partial probability distribution modeling by focusing computation on the most relevant age ranges rather than modeling the complete age distribution with equal precision. The model outputs probabilities for discrete age bins rather than continuous distribution parameters, implementing a simplified form of distribution modeling that captures uncertainty without full computational complexity.
Solution Approach 2:
The patent simplifies the probability distribution representation by changing from continuous distribution parameters to discrete probability bins. Instead of modeling continuous probability density functions, the system uses categorical probability distributions over predefined age ranges, reducing the computational parameters while maintaining the ability to represent uncertainty and ambiguity.
Data Source
AI summary
Disclosed are systems and methods including computing-processes executing machine-learning architectures implementing label distribution loss functions to improve age estimation performance and generalization. The machine-learning architecture includes a front-end neural network architecture defining a speaker embedding extraction engine of the machine-learning architecture, and a backend neural network architecture defining an age estimation engine of the machine-learning architecture. The embedding extractor is trained to extract low-level acoustic features of a speaker's speech, such as mel-frequency cepstral coefficients (MFCCs), from audio signals, and then extract a feature vector or speaker embedding vector that mathematically represents the low-level features of the speaker. The age estimator is trained to generate an estimated age for the speaker and a Gaussian probability distribution around the estimated age, by applying the various types of layers of the age estimator on the speaker embedding.