Hierarchical attention interpretation: an interpretable speech-level transformer for bi-modal depression detection
Depression is a common mental disorder. Automatic depression detection tools using speech, enabled by machine learning, help early screening of depression. This paper addresses two limitations that may hinder the clinical implementations of such tools: noise resulting from segment-level labelling and a lack of model interpretability. We propose a bi-modal speech-level transformer to avoid segment-level labelling and introduce a hierarchical interpretation approach to provide both speech-level and sentence-level interpretations, based on gradient-weighted attention maps derived from all attention layers to track interactions between input features. We show that the proposed model outperforms a model that learns at a segment level ($p$=0.854, $r$=0.947, $F1$=0.897 compared to $p$=0.732, $r$=0.808, $F1$=0.768). For model interpretation, using one true positive sample, we show which sentences within a given speech are most relevant to depression detection; and which text tokens and Mel-spectrogram regions within these sentences are most relevant to depression detection. These interpretations allow clinicians to verify the validity of predictions made by depression detection tools, promoting their clinical implementations.
Code (0)
등록된 구현이 없습니다.
Tasks
Depression DetectionSentenceSimilar Papers 제목 키워드 기반
Fully-hierarchical fine-grained prosody modeling for interpretable speech synthesis
This paper proposes a hierarchical, fine-grained and interpretable latent variable model for prosody based on the Tacotron 2 text-to-speech model. It achieves multi-resolution modeling of prosody by conditioning finer le…
DisentanglementSpeech Synthesistext-to-speechText to SpeechHierarchical Interpretation of Neural Text Classification
Recent years have witnessed increasing interests in developing interpretable models in Natural Language Processing (NLP). Most existing models aim at identifying input features such as words or phrases important for mode…
Classificationtext-classificationText ClassificationUnlocking Biomedical Insights: Hierarchical Attention Networks for High-Dimensional Data Interpretation
The proliferation of high-dimensional datasets in fields such as genomics, healthcare, and finance has created an urgent need for machine learning models that are both highly accurate and inherently interpretable. While …
Dimensionality ReductionInterpretable Syntactic Representations Enable Hierarchical Word Vectors
The distributed representations currently used are dense and uninterpretable, leading to interpretations that themselves are relative, overcomplete, and hard to interpret. We propose a method that transforms these word v…
Incremental LearningHAN-ECG: An Interpretable Atrial Fibrillation Detection Model Using Hierarchical Attention Networks
Atrial fibrillation (AF) is one of the most prevalent cardiac arrhythmias that affects the lives of more than 3 million people in the U.S. and over 33 million people around the world and is associated with a five-fold in…
Atrial Fibrillation Detection