Hierarchical attention interpretation: an interpretable speech-level
transformer for bi-modal depression detection
- URL: http://arxiv.org/abs/2309.13476v2
- Date: Fri, 6 Oct 2023 11:46:11 GMT
- Title: Hierarchical attention interpretation: an interpretable speech-level
transformer for bi-modal depression detection
- Authors: Qingkun Deng, Saturnino Luz, Sofia de la Fuente Garcia
- Abstract summary: 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.
- Score: 6.561362931802501
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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.
Related papers
- Predicting Individual Depression Symptoms from Acoustic Features During Speech [8.592847632589692]
Current automatic depression detection systems provide predictions directly without relying on the individual symptoms/items of depression as denoted in the clinical depression rating scales.
In this work, we make a first step towards using the acoustic features of speech to predict individual items of the depression rating scale before obtaining the final depression prediction.
arXiv Detail & Related papers (2024-06-23T03:26:47Z) - DepressionEmo: A novel dataset for multilabel classification of
depression emotions [6.26397257917403]
DepressionEmo is a dataset designed to detect 8 emotions associated with depression by 6037 examples of long Reddit user posts.
This dataset was created through a majority vote over inputs by zero-shot classifications from pre-trained models.
We provide several text classification methods classified into two groups: machine learning methods such as SVM, XGBoost, and Light GBM; and deep learning methods such as BERT, GAN-BERT, and BART.
arXiv Detail & Related papers (2024-01-09T16:25:31Z) - Identifying depression-related topics in smartphone-collected
free-response speech recordings using an automatic speech recognition system
and a deep learning topic model [7.825530847570242]
We identified 29 topics in 3919 smartphone-collected speech recordings from 265 participants.
Six topics with a median PHQ-8 greater than or equal to 10 were regarded as risk topics for depression.
The correlation between topic shifts and changes in depression severity over time was also investigated.
arXiv Detail & Related papers (2023-08-22T20:30:59Z) - The Relationship Between Speech Features Changes When You Get Depressed:
Feature Correlations for Improving Speed and Performance of Depression
Detection [69.88072583383085]
This work shows that depression changes the correlation between features extracted from speech.
Using such an insight can improve the training speed and performance of depression detectors based on SVMs and LSTMs.
arXiv Detail & Related papers (2023-07-06T09:54:35Z) - Depression detection in social media posts using affective and social
norm features [84.12658971655253]
We propose a deep architecture for depression detection from social media posts.
We incorporate profanity and morality features of posts and words in our architecture using a late fusion scheme.
The inclusion of the proposed features yields state-of-the-art results in both settings.
arXiv Detail & Related papers (2023-03-24T21:26:27Z) - Semantic Similarity Models for Depression Severity Estimation [53.72188878602294]
This paper presents an efficient semantic pipeline to study depression severity in individuals based on their social media writings.
We use test user sentences for producing semantic rankings over an index of representative training sentences corresponding to depressive symptoms and severity levels.
We evaluate our methods on two Reddit-based benchmarks, achieving 30% improvement over state of the art in terms of measuring depression severity.
arXiv Detail & Related papers (2022-11-14T18:47:26Z) - Speaker Embedding-aware Neural Diarization for Flexible Number of
Speakers with Textual Information [55.75018546938499]
We propose the speaker embedding-aware neural diarization (SEND) method, which predicts the power set encoded labels.
Our method achieves lower diarization error rate than the target-speaker voice activity detection.
arXiv Detail & Related papers (2021-11-28T12:51:04Z) - Deep Multi-task Learning for Depression Detection and Prediction in
Longitudinal Data [50.02223091927777]
Depression is among the most prevalent mental disorders, affecting millions of people of all ages globally.
Machine learning techniques have shown effective in enabling automated detection and prediction of depression for early intervention and treatment.
We introduce a novel deep multi-task recurrent neural network to tackle this challenge, in which depression classification is jointly optimized with two auxiliary tasks.
arXiv Detail & Related papers (2020-12-05T05:14:14Z) - Detecting Hallucinated Content in Conditional Neural Sequence Generation [165.68948078624499]
We propose a task to predict whether each token in the output sequence is hallucinated (not contained in the input)
We also introduce a method for learning to detect hallucinations using pretrained language models fine tuned on synthetic data.
arXiv Detail & Related papers (2020-11-05T00:18:53Z) - Multimodal Depression Severity Prediction from medical bio-markers using
Machine Learning Tools and Technologies [0.0]
Depression has been a leading cause of mental-health illnesses across the world.
Using behavioural cues to automate depression diagnosis and stage prediction in recent years has relatively increased.
The absence of labelled behavioural datasets and a vast amount of possible variations prove to be a major challenge in accomplishing the task.
arXiv Detail & Related papers (2020-09-11T20:44:28Z) - Affective Conditioning on Hierarchical Networks applied to Depression
Detection from Transcribed Clinical Interviews [0.0]
Depression is a mental disorder that impacts not only the subject's mood but also the use of language.
We use a Hierarchical Attention Network to classify interviews of depressed subjects.
We augment the attention layer of our model with a conditioning mechanism on linguistic features, extracted from affective lexica.
arXiv Detail & Related papers (2020-06-04T20:55:22Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.