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

09/14/2020
by   Vaibhav Sourirajan, et al.
0

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.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
11/15/2020

Respiratory Distress Detection from Telephone Speech using Acoustic and Prosodic Features

With the widespread use of telemedicine services, automatic assessment o...
research
04/20/2023

Emotional Expression Detection in Spoken Language Employing Machine Learning Algorithms

There are a variety of features of the human voice that can be classifie...
research
05/05/2022

Sound Event Classification in an Industrial Environment: Pipe Leakage Detection Use Case

In this work, a multi-stage Machine Learning (ML) pipeline is proposed f...
research
12/24/2020

Detecting Hateful Memes Using a Multimodal Deep Ensemble

While significant progress has been made using machine learning algorith...
research
05/23/2023

Happy or Evil Laughter? Analysing a Database of Natural Audio Samples

We conducted a data collection on the basis of the Google AudioSet datab...
research
07/19/2023

Alzheimer's Disease Detection from Spontaneous Speech and Text: A review

In the past decade, there has been a surge in research examining the use...
research
08/01/2023

Beam Detection Based on Machine Learning Algorithms

The positions of free electron laser beams on screens are precisely dete...

Please sign up or login with your details

Forgot password? Click here to reset