Density Adaptive Attention-based Speech Network: Enhancing Feature
  Understanding for Mental Health Disorders
Speech-based depression detection poses significant challenges for automated
detection due to its unique manifestation across individuals and data scarcity.
Addressing these challenges, we introduce DAAMAudioCNNLSTM and
DAAMAudioTransformer, two parameter efficient and explainable models for audio
feature extraction and depression detection. DAAMAudioCNNLSTM features a novel
CNN-LSTM framework with multi-head Density Adaptive Attention Mechanism (DAAM),
focusing dynamically on informative speech segments. DAAMAudioTransformer,
leveraging a transformer encoder in place of the CNN-LSTM architecture,
incorporates the same DAAM module for enhanced attention and interpretability.
These approaches not only enhance detection robustness and interpretability but
also achieve state-of-the-art performance: DAAMAudioCNNLSTM with an F1 macro
score of 0.702 and DAAMAudioTransformer with an F1 macro score of 0.72 on the
DAIC-WOZ dataset, without reliance on supplementary information such as vowel
positions and speaker information during training/validation as in previous
approaches. Both models' significant explainability and efficiency in
leveraging speech signals for depression detection represent a leap towards
more reliable, clinically useful diagnostic tools, promising advancements in
speech and mental health care. To foster further research in this domain, we
make our code publicly available.
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Georgios Ioannides
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Adrian Kieback
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2025-04-08T18:53:05.402Z
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Kieback
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KiebackA
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Aman Chadha
claimed_verified
2024-09-04T07:15:06.838Z
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Aman Chadha
user
amanchadha
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Aaron Elkins
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2024-09-04T03:55:55.087Z
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Aaron Elkins
user
jsbai-aaron
Density Adaptive Attention-based Speech Network: Enhancing Feature
  Understanding for Mental Health Disorders
Speech-based depression detection poses significant challenges for automated
detection due to its unique manifestation across individuals and data scarcity.
Addressing these challenges, we introduce DAAMAudioCNNLSTM and
DAAMAudioTransformer, two parameter efficient and explainable models for audio
feature extraction and depression detection. DAAMAudioCNNLSTM features a novel
CNN-LSTM framework with multi-head Density Adaptive Attention Mechanism (DAAM),
focusing dynamically on informative speech segments. DAAMAudioTransformer,
leveraging a transformer encoder in place of the CNN-LSTM architecture,
incorporates the same DAAM module for enhanced attention and interpretability.
These approaches not only enhance detection robustness and interpretability but
also achieve state-of-the-art performance: DAAMAudioCNNLSTM with an F1 macro
score of 0.702 and DAAMAudioTransformer with an F1 macro score of 0.72 on the
DAIC-WOZ dataset, without reliance on supplementary information such as vowel
positions and speaker information during training/validation as in previous
approaches. Both models' significant explainability and efficiency in
leveraging speech signals for depression detection represent a leap towards
more reliable, clinically useful diagnostic tools, promising advancements in
speech and mental health care. To foster further research in this domain, we
make our code publicly available.
Two parameter-efficient and explainable models, DAAMAudioCNNLSTM and DAAMAudioTransformer, achieve state-of-the-art performance in speech-based depression detection using novel attention mechanisms without supplementary information.
DAAMAudioCNNLSTM DAAMAudioTransformer CNN-LSTM Density Adaptive Attention Mechanism (DAAM) transformer encoder F1 macro score DAIC-WOZ dataset