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Recognition of Dysarthria in Amyotrophic Lateral Sclerosis patients using Hypernetworks

Loukas Ilias, Dimitris Askounis

TL;DR

The paper tackles dysarthria recognition in ALS from speech using hypernetworks to enable input-conditioned weight generation. It processes speech as a 3-channel image (log-Mel spectrogram, delta, delta-delta) and passes it through a pretrained encoder to obtain a feature vector $X \in \mathbb{R}^{768}$, then uses a hypernetwork with context $C \sim \mathcal{N}(0,I)$ to produce weights $\\Theta = H(C; \\Phi)$ for a binary classifier $F(X;\\Theta)$. On the VOC-ALS dataset, the approach achieves an accuracy of $82.66\%$, outperforming strong baselines, with ablation confirming the hypernetwork's contribution to generalization and parameter efficiency. Limitations include dataset imbalance, and the authors suggest neural architecture search as a future direction to further optimize the model.

Abstract

Amyotrophic Lateral Sclerosis (ALS) constitutes a progressive neurodegenerative disease with varying symptoms, including decline in speech intelligibility. Existing studies, which recognize dysarthria in ALS patients by predicting the clinical standard ALSFRS-R, rely on feature extraction strategies and the design of customized convolutional neural networks followed by dense layers. However, recent studies have shown that neural networks adopting the logic of input-conditional computations enjoy a series of benefits, including faster training, better performance, and flexibility. To resolve these issues, we present the first study incorporating hypernetworks for recognizing dysarthria. Specifically, we use audio files, convert them into log-Mel spectrogram, delta, and delta-delta, and pass the resulting image through a pretrained modified AlexNet model. Finally, we use a hypernetwork, which generates weights for a target network. Experiments are conducted on a newly collected publicly available dataset, namely VOC-ALS. Results showed that the proposed approach reaches Accuracy up to 82.66% outperforming strong baselines, including multimodal fusion methods, while findings from an ablation study demonstrated the effectiveness of the introduced methodology. Overall, our approach incorporating hypernetworks obtains valuable advantages over state-of-the-art results in terms of generalization ability, parameter efficiency, and robustness.

Recognition of Dysarthria in Amyotrophic Lateral Sclerosis patients using Hypernetworks

TL;DR

The paper tackles dysarthria recognition in ALS from speech using hypernetworks to enable input-conditioned weight generation. It processes speech as a 3-channel image (log-Mel spectrogram, delta, delta-delta) and passes it through a pretrained encoder to obtain a feature vector , then uses a hypernetwork with context to produce weights for a binary classifier . On the VOC-ALS dataset, the approach achieves an accuracy of , outperforming strong baselines, with ablation confirming the hypernetwork's contribution to generalization and parameter efficiency. Limitations include dataset imbalance, and the authors suggest neural architecture search as a future direction to further optimize the model.

Abstract

Amyotrophic Lateral Sclerosis (ALS) constitutes a progressive neurodegenerative disease with varying symptoms, including decline in speech intelligibility. Existing studies, which recognize dysarthria in ALS patients by predicting the clinical standard ALSFRS-R, rely on feature extraction strategies and the design of customized convolutional neural networks followed by dense layers. However, recent studies have shown that neural networks adopting the logic of input-conditional computations enjoy a series of benefits, including faster training, better performance, and flexibility. To resolve these issues, we present the first study incorporating hypernetworks for recognizing dysarthria. Specifically, we use audio files, convert them into log-Mel spectrogram, delta, and delta-delta, and pass the resulting image through a pretrained modified AlexNet model. Finally, we use a hypernetwork, which generates weights for a target network. Experiments are conducted on a newly collected publicly available dataset, namely VOC-ALS. Results showed that the proposed approach reaches Accuracy up to 82.66% outperforming strong baselines, including multimodal fusion methods, while findings from an ablation study demonstrated the effectiveness of the introduced methodology. Overall, our approach incorporating hypernetworks obtains valuable advantages over state-of-the-art results in terms of generalization ability, parameter efficiency, and robustness.

Paper Structure

This paper contains 14 sections, 2 equations, 1 figure, 3 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Traditional Machine Learning Algorithms
  4. Deep Neural Networks
  5. Related Work Review Findings
  6. Dataset and Task
  7. Methodology
  8. Experiments and Results
  9. Baselines
  10. Experimental Setup
  11. Evaluation Metrics
  12. Results
  13. Ablation Study
  14. Conclusion

Figures (1)

  • Figure 1: Illustration of our proposed methodology. Each speech signal is transformed into log-Mel spectrogram, delta, and delta-delta, and is given as input to a pretrained AlexNet model. The output vector of the AlexNet model with a dimensionality of 768 is given as input to a target network ($F(X; \Theta$), where its weights are generated by a hypernetwork ($H(C; \Phi)$). The input to the hypernetwork is denoted by $C$ and follows a normal distribution. Finally, we use an output layer consisting of two units, which differentiates dysarthric from non-dysarthric ALS patients.