Neck-Learn: Attention-Based Multiple Instance Learning and Ensemble Framework for Ecological Momentary Assessment
Abstract
Vocal hyperfunction (VH) is a prevalent voice disorder whose ambulatory detection remains challenging despite extensive daily voice data. Prior approaches capture week-long neck-surface accelerometer recordings but collapse them into fixed-length subject-level feature vectors, discarding within-day temporal dynamics encoding nuanced voicing feature interactions. We introduce a novel hybrid architecture combining gradient-boosted trees on day-level distributional features with a CNN-based multiple instance learning (MIL) framework that preserves and learns from from temporal dynamics throughout each day. On the held-out test set, our model exceeds the challenge baselines (AUC: 0.82 PVH, 0.77 NPVH), achieving AUCs of 0.879 for PVH (Rank 5) and 0.848 for NPVH (Rank 3), while also providing insights into clinically relevant information about both pathologies.
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
The paper presents a novel hybrid architecture for detecting vocal hyperfunction using attention-based multiple instance learning, significantly advancing the state of the art in ambulatory voice monitoring. The methodology effectively addresses the limitations of previous approaches by preserving temporal dynamics, leading to improved diagnostic accuracy and insights into voice disorders.
Methodology
The paper introduces a hybrid architecture that effectively combines gradient-boosted trees with a CNN-based multiple instance learning (MIL) framework, which is innovative in the context of ecological momentary assessment for voice disorders. The dual-representation framework allows the model to leverage both global distributional features and local temporal dynamics, addressing the shortcomings of previous methods that relied on fixed-length feature vectors. The attention mechanism in the CNN-MIL architecture is particularly noteworthy, as it enables the model to learn which time segments are most discriminative for vocal hyperfunction detection.
Results & evaluation
The experiments are robust, utilizing the NeckVibe Challenge dataset, which is the largest publicly available dataset for this task. The reported AUC scores demonstrate significant improvements over existing baselines, particularly for the more challenging non-structural vocal hyperfunction (NPVH) classification. The systematic ablation studies provide strong evidence for the contributions of each model component, confirming the effectiveness of the proposed ensemble strategy.
Reproducibility
The paper provides detailed implementation information, including hyperparameters and software versions used, which enhances reproducibility. However, the absence of a publicly accessible code repository limits the ability for other researchers to replicate the results fully.
Limitations
The study acknowledges that the CNN-MIL framework processes each day independently, which may overlook week-level trends in vocal behavior. Additionally, while the attention mechanism offers some interpretability, the complexity of the model may hinder full understanding of the feature interactions. Future work could explore hierarchical models and causal inference approaches.
Broader impact
The findings have significant implications for the field of voice disorder diagnostics, particularly in enhancing ambulatory monitoring techniques. By capturing temporal dynamics, the proposed approach could lead to more accurate and timely interventions for individuals suffering from vocal hyperfunction, potentially improving patient outcomes. The paper presents a novel hybrid architecture for detecting vocal hyperfunction using attention-based multiple instance learning, significantly advancing the state of the art in ambulatory voice monitoring. The methodology effectively addresses the limitations of previous approaches by preserving temporal dynamics, leading to improved diagnostic accuracy and insights into voice disorders.
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