OK Aura, Be Fair With Me: Demographics-Agnostic Training for Bias Mitigation in Wake-up Word Detection
Summary: arXiv:2604.05830v1 Announce Type: cross
Abstract
Voice-based interfaces are widely used; however, achieving fair Wake-up Word detection across diverse speaker populations remains a critical challenge due to persistent demographic biases. This study evaluates the effectiveness of demographics-agnostic training techniques in mitigating performance disparities among speakers of varying sex, age, and accent.
Introduction
The increase in voice-activated devices has revolutionized how users interact with technology. However, the effectiveness of these systems can be significantly compromised by demographic biases that affect Wake-up Word detection. This article discusses a recent study that focuses on training methodologies that do not rely on demographic labels to foster fairness across different speaker groups.
Methodology
In our experiments, we utilized the OK Aura database, which is specifically designed for Wake-up Word detection tasks. The study employed a training methodology that excludes demographic labels, which are only utilized for evaluation purposes. This approach allows for a more generalized model that is not biased by the demographic characteristics of the speakers.
Key Techniques
- Data Augmentation Techniques: These techniques enhance model generalization by artificially increasing the diversity of the training dataset. By introducing variations in the input data, the model learns to recognize Wake-up Words more effectively across different demographics.
- Knowledge Distillation: This involves transferring knowledge from pre-trained foundational speech models to the new model. It enables the new model to leverage the strengths of existing models while focusing on minimizing demographic bias.
Results
The experimental results indicate that the demographics-agnostic training techniques markedly reduce demographic bias, leading to a more equitable performance profile across different speaker groups. Specifically, one of the evaluated techniques achieved:
- Predictive Disparity Reduction for Sex: 39.94%
- Predictive Disparity Reduction for Age: 83.65%
- Predictive Disparity Reduction for Accent: 40.48%
These results demonstrate the significant impact of demographics-agnostic training on improving fairness in Wake-up Word detection systems.
Conclusion
This study highlights the effectiveness of label-agnostic methodologies in fostering fairness in Wake-up Word detection. By employing techniques such as data augmentation and knowledge distillation, developers can create more equitable voice recognition systems that perform consistently well across diverse speaker populations. The findings suggest that future research should continue to explore demographics-agnostic strategies to further reduce bias and enhance user experience in voice-activated technologies.
Future Work
Continued exploration into the realms of demographics-agnostic training will be essential. Future studies could focus on:
- Expanding the diversity of training datasets.
- Investigating additional machine learning techniques for bias mitigation.
- Implementing real-world testing to evaluate the practical applications of these methodologies.