A Machine Learning Approach to Detect Suicidal Ideation in US Veterans Based on Acoustic and Linguistic Features of Speech

• URL: http://arxiv.org/abs/2009.09069v2
• Date: Sun, 27 Sep 2020 17:36:49 GMT
• Title: A Machine Learning Approach to Detect Suicidal Ideation in US Veterans Based on Acoustic and Linguistic Features of Speech
• Authors: Vaibhav Sourirajan, Anas Belouali, Mary Ann Dutton, Matthew Reinhard, Jyotishman Pathak
• Abstract summary: Speech analysis in a machine learning pipeline is a promising approach for detecting suicidality among Veterans. Our study shows that speech analysis in a machine learning pipeline is a promising approach for detecting suicidality among Veterans.
• Score: 1.402448064801195
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Preventing Veteran suicide is a national priority. The US Department of Veterans Affairs (VA) collects, analyzes, and publishes data to inform suicide prevention strategies. Current approaches for detecting suicidal ideation mostly rely on patient self report which are inadequate and time consuming. In this research study, our goal was to automate suicidal ideation detection from acoustic and linguistic features of an individual's speech using machine learning (ML) algorithms. Using voice data collected from Veterans enrolled in a large interventional study on Gulf War Illness at the Washington DC VA Medical Center, we conducted an evaluation of the performance of different ML approaches in achieving our objective. By fitting both classical ML and deep learning models to the dataset, we identified the algorithms that were most effective for each feature set. Among classical machine learning algorithms, the Support Vector Machine (SVM) trained on acoustic features performed best in classifying suicidal Veterans. Among deep learning methods, the Convolutional Neural Network (CNN) trained on the linguistic features performed best. Our study shows that speech analysis in a machine learning pipeline is a promising approach for detecting suicidality among Veterans.

Related papers

• Self-supervised Speech Models for Word-Level Stuttered Speech Detection [66.46810024006712]
  We introduce a word-level stuttering speech detection model leveraging self-supervised speech models. Our evaluation demonstrates that our model surpasses previous approaches in word-level stuttering speech detection.
  arXiv  Detail & Related papers  (2024-09-16T20:18:20Z)
• Deep Learning and Large Language Models for Audio and Text Analysis in Predicting Suicidal Acts in Chinese Psychological Support Hotlines [13.59130559079134]
  Approximately two million people in China attempt suicide annually, with many individuals making multiple attempts. Deep learning models and large-scale language models (LLMs) have been introduced to the field of mental health. This study included 1284 subjects, and was designed to validate whether deep learning models and LLMs, using audio and transcribed text from support hotlines, can effectively predict suicide risk.
  arXiv  Detail & Related papers  (2024-09-10T02:22:50Z)
• 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)
• Exploring Multimodal Approaches for Alzheimer's Disease Detection Using Patient Speech Transcript and Audio Data [10.782153332144533]
  Alzheimer's disease (AD) is a common form of dementia that severely impacts patient health. This study investigates various methods for detecting AD using patients' speech and transcripts data from the DementiaBank Pitt database.
  arXiv  Detail & Related papers  (2023-07-05T12:40:11Z)
• Leveraging Pretrained Representations with Task-related Keywords for Alzheimer's Disease Detection [69.53626024091076]
  Alzheimer's disease (AD) is particularly prominent in older adults. Recent advances in pre-trained models motivate AD detection modeling to shift from low-level features to high-level representations. This paper presents several efficient methods to extract better AD-related cues from high-level acoustic and linguistic features.
  arXiv  Detail & Related papers  (2023-03-14T16:03:28Z)
• Multilingual Alzheimer's Dementia Recognition through Spontaneous Speech: a Signal Processing Grand Challenge [18.684024762601215]
  This Signal Processing Grand Challenge (SPGC) targets a difficult automatic prediction problem of societal and medical relevance. The Challenge has been designed to assess the extent to which predictive models built based on speech in one language (English) generalise to another language (Greek)
  arXiv  Detail & Related papers  (2023-01-13T14:09:13Z)
• Decoding speech perception from non-invasive brain recordings [48.46819575538446]
  We introduce a model trained with contrastive-learning to decode self-supervised representations of perceived speech from non-invasive recordings. Our model can identify, from 3 seconds of MEG signals, the corresponding speech segment with up to 41% accuracy out of more than 1,000 distinct possibilities.
  arXiv  Detail & Related papers  (2022-08-25T10:01:43Z)
• Zero-Shot Cross-lingual Aphasia Detection using Automatic Speech Recognition [3.2631198264090746]
  Aphasia is a common speech and language disorder, typically caused by a brain injury or a stroke, that affects millions of people worldwide. We propose an end-to-end pipeline using pre-trained Automatic Speech Recognition (ASR) models that share cross-lingual speech representations.
  arXiv  Detail & Related papers  (2022-04-01T14:05:02Z)
• Deep Learning for Hate Speech Detection: A Comparative Study [54.42226495344908]
  We present here a large-scale empirical comparison of deep and shallow hate-speech detection methods. Our goal is to illuminate progress in the area, and identify strengths and weaknesses in the current state-of-the-art. In doing so we aim to provide guidance as to the use of hate-speech detection in practice, quantify the state-of-the-art, and identify future research directions.
  arXiv  Detail & Related papers  (2022-02-19T03:48:20Z)
• NUVA: A Naming Utterance Verifier for Aphasia Treatment [49.114436579008476]
  Assessment of speech performance using picture naming tasks is a key method for both diagnosis and monitoring of responses to treatment interventions by people with aphasia (PWA) Here we present NUVA, an utterance verification system incorporating a deep learning element that classifies 'correct' versus'incorrect' naming attempts from aphasic stroke patients. When tested on eight native British-English speaking PWA the system's performance accuracy ranged between 83.6% to 93.6%, with a 10-fold cross-validation mean of 89.5%.
  arXiv  Detail & Related papers  (2021-02-10T13:00:29Z)
• Multi-Modal Detection of Alzheimer's Disease from Speech and Text [3.702631194466718]
  We propose a deep learning method that utilizes speech and the corresponding transcript simultaneously to detect Alzheimer's disease (AD) The proposed method achieves 85.3% 10-fold cross-validation accuracy when trained and evaluated on the Dementiabank Pitt corpus.
  arXiv  Detail & Related papers  (2020-11-30T21:18:17Z)

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.