Exploring Gender-Specific Speech Patterns in Automatic Suicide Risk Assessment

• URL: http://arxiv.org/abs/2407.11012v1
• Date: Wed, 26 Jun 2024 12:51:28 GMT
• Title: Exploring Gender-Specific Speech Patterns in Automatic Suicide Risk Assessment
• Authors: Maurice Gerczuk, Shahin Amiriparian, Justina Lutz, Wolfgang Strube, Irina Papazova, Alkomiet Hasan, Björn W. Schuller,
• Abstract summary: This study involves a novel dataset comprising speech recordings of 20 patients who read neutral texts. We extract four speech representations encompassing interpretable and deep features. By applying gender-exclusive modelling, features extracted from an emotion fine-tuned wav2vec2.0 model can be utilised to discriminate high- from low- suicide risk with a balanced accuracy of 81%.
• Score: 39.26231968260796
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: In emergency medicine, timely intervention for patients at risk of suicide is often hindered by delayed access to specialised psychiatric care. To bridge this gap, we introduce a speech-based approach for automatic suicide risk assessment. Our study involves a novel dataset comprising speech recordings of 20 patients who read neutral texts. We extract four speech representations encompassing interpretable and deep features. Further, we explore the impact of gender-based modelling and phrase-level normalisation. By applying gender-exclusive modelling, features extracted from an emotion fine-tuned wav2vec2.0 model can be utilised to discriminate high- from low- suicide risk with a balanced accuracy of 81%. Finally, our analysis reveals a discrepancy in the relationship of speech characteristics and suicide risk between female and male subjects. For men in our dataset, suicide risk increases together with agitation while voice characteristics of female subjects point the other way.

Related papers

• Towards Probing Speech-Specific Risks in Large Multimodal Models: A Taxonomy, Benchmark, and Insights [50.89022445197919]
  We propose a speech-specific risk taxonomy, covering 8 risk categories under hostility (malicious sarcasm and threats), malicious imitation (age, gender, ethnicity), and stereotypical biases (age, gender, ethnicity) Based on the taxonomy, we create a small-scale dataset for evaluating current LMMs capability in detecting these categories of risk.
  arXiv  Detail & Related papers  (2024-06-25T10:08:45Z)
• Spontaneous Speech-Based Suicide Risk Detection Using Whisper and Large Language Models [5.820498448651539]
  This paper studies the automatic detection of suicide risk based on spontaneous speech from adolescents. The proposed system achieves a detection accuracy of 0.807 and an F1-score of 0.846 on the test set with 119 subjects.
  arXiv  Detail & Related papers  (2024-06-06T09:21:13Z)
• SOS-1K: A Fine-grained Suicide Risk Classification Dataset for Chinese Social Media Analysis [22.709733830774788]
  This study presents a Chinese social media dataset designed for fine-grained suicide risk classification. Seven pre-trained models were evaluated in two tasks: high and low suicide risk, and fine-grained suicide risk classification on a level of 0 to 10. Deep learning models show good performance in distinguishing between high and low suicide risk, with the best model achieving an F1 score of 88.39%.
  arXiv  Detail & Related papers  (2024-04-19T06:58:51Z)
• Non-Invasive Suicide Risk Prediction Through Speech Analysis [74.8396086718266]
  We present a non-invasive, speech-based approach for automatic suicide risk assessment. We extract three sets of features, including wav2vec, interpretable speech and acoustic features, and deep learning-based spectral representations. Our most effective speech model achieves a balanced accuracy of $66.2,%$.
  arXiv  Detail & Related papers  (2024-04-18T12:33:57Z)
• Detecting Suicide Risk in Online Counseling Services: A Study in a Low-Resource Language [5.2636083103718505]
  We propose a model that combines pre-trained language models (PLM) with a fixed set of manually crafted (and clinically approved) set of suicidal cues. Our model achieves 0.91 ROC-AUC and an F2-score of 0.55, significantly outperforming an array of strong baselines even early on in the conversation.
  arXiv  Detail & Related papers  (2022-09-11T10:06:14Z)
• Am I No Good? Towards Detecting Perceived Burdensomeness and Thwarted Belongingness from Suicide Notes [51.378225388679425]
  We present an end-to-end multitask system to address a novel task of detection of Perceived Burdensomeness (PB) and Thwarted Belongingness (TB) from suicide notes. We also introduce a manually translated code-mixed suicide notes corpus, CoMCEASE-v2.0, based on the benchmark CEASE-v2.0 dataset. We exploit the temporal orientation and emotion information in the suicide notes to boost overall performance.
  arXiv  Detail & Related papers  (2022-05-20T06:31:08Z)
• Naturalistic Causal Probing for Morpho-Syntax [76.83735391276547]
  We suggest a naturalistic strategy for input-level intervention on real world data in Spanish. Using our approach, we isolate morpho-syntactic features from counfounders in sentences. We apply this methodology to analyze causal effects of gender and number on contextualized representations extracted from pre-trained models.
  arXiv  Detail & Related papers  (2022-05-14T11:47:58Z)
• Mitigating Biases in Toxic Language Detection through Invariant Rationalization [70.36701068616367]
  biases toward some attributes, including gender, race, and dialect, exist in most training datasets for toxicity detection. We propose to use invariant rationalization (InvRat), a game-theoretic framework consisting of a rationale generator and a predictor, to rule out the spurious correlation of certain syntactic patterns. Our method yields lower false positive rate in both lexical and dialectal attributes than previous debiasing methods.
  arXiv  Detail & Related papers  (2021-06-14T08:49:52Z)
• Indirect Identification of Psychosocial Risks from Natural Language [0.0]
  psychosocial health risks, including depression and intimate partner violence, are associated with serious adverse health outcomes for parents and children. We examine indirect methods of eliciting and analyzing information that could indicate psychosocial risks. Regularized regression is used to predict screening measures of depression and psychological aggression by an intimate partner.
  arXiv  Detail & Related papers  (2020-04-30T03:13:28Z)

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.