title: Density Adaptive Attention-based Speech Network: Enhancing Feature
    Understanding for Mental Health Disorders
summary: 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.
paper_authors._id: 66d7da4a3c79020aa681b2d2
paper_authors.name: Georgios Ioannides
paper_authors._id: 66d7da4a3c79020aa681b2d3
paper_authors.name: Adrian Kieback
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paper_authors.fullname: Kieback
paper_authors.type: user
paper_authors.user: KiebackA
paper_authors._id: 66d7da4a3c79020aa681b2d4
paper_authors.name: Aman Chadha
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paper_authors.fullname: Aman Chadha
paper_authors.type: user
paper_authors.user: amanchadha
paper_authors._id: 66d7da4a3c79020aa681b2d5
paper_authors.name: Aaron Elkins
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paper_authors._id: 6615cf1a0ba998609c977de4
paper_authors.avatarUrl: /avatars/42bd9bc14302976fee87adc04f3dbdc5.svg
paper_authors.fullname: Aaron Elkins
paper_authors.type: user
paper_authors.user: jsbai-aaron
paper_title: Density Adaptive Attention-based Speech Network: Enhancing Feature
    Understanding for Mental Health Disorders
paper_summary: 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.
paper_ai_summary: 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.
paper_ai_keywords: DAAMAudioCNNLSTM DAAMAudioTransformer CNN-LSTM Density Adaptive Attention Mechanism (DAAM) transformer encoder F1 macro score DAIC-WOZ dataset