Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Classification of Signals01:30

Classification of Signals

417
In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
417
Cognitive Therapy01:25

Cognitive Therapy

146
Cognitive therapy, pioneered by Aaron T. Beck in the 1960s, is a structured approach to addressing psychological distress by focusing on the influence of thoughts on emotions and behaviors. All cognitive therapies involve the basic assumption that human beings have control over their feelings, and that how individuals feel about something depends on how they think about it. Unlike psychoanalytic methods that delve into unconscious processes or humanistic approaches emphasizing...
146

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Friend and Confidant Thresholds: Social Network Size as a Mediator Between Marital Status and Major Depressive Disorder.

Depression and anxiety·2026
Same author

Lobectomy, segmentectomy, and wedge resection for elderly patients with solid-predominant stage I NSCLC: survival, pulmonary function, and postoperative outcomes.

Translational lung cancer research·2026
Same author

Did frailty influence older adults' choices of communication methods during COVID-19?

BMC geriatrics·2026
Same author

External validation of cough-based algorithms for pulmonary tuberculosis screening from the CODA TB DREAM challenge using cough data from Peru.

Scientific reports·2026
Same author

A Global Prospective Harmonization Framework for Suicidality, Anhedonia, and Obsessive-Compulsive Symptoms in Psychiatric Genetic Studies: A Cross-Continental Study Within the Ancestral Population Network.

American journal of medical genetics. Part B, Neuropsychiatric genetics : the official publication of the International Society of Psychiatric Genetics·2026
Same author

Uridine diphosphate glucose reduces embryonic and diet-induced hepatic lipid accumulation in chickens.

Research in veterinary science·2026

Related Experiment Video

Updated: Jun 11, 2025

Author Spotlight: Therapeutic Benefit of Closed-Loop Deep Brain Stimulation in Depression Treatment
05:19

Author Spotlight: Therapeutic Benefit of Closed-Loop Deep Brain Stimulation in Depression Treatment

Published on: July 7, 2023

2.2K

Speechformer-CTC: Sequential Modeling of Depression Detection with Speech Temporal Classification.

Jinhan Wang1, Vijay Ravi1, Jonathan Flint2

  • 1Department of Electrical and Computer Engineering, University of California, Los Angeles, California, 90095, USA.

Speech Communication
|October 4, 2024
PubMed
Summary

This study introduces Speechformer-CTC, a novel framework for depression detection in speech. It effectively models subtle depression patterns within speech segments, improving accuracy on English and Mandarin datasets without needing detailed transcriptions.

Keywords:
CTCDepression-detectionNon-uniform distribution

More Related Videos

Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception
05:48

Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception

Published on: August 9, 2024

1.4K
Individualized rTMS Treatment for Depression using an fMRI-Based Targeting Method
07:12

Individualized rTMS Treatment for Depression using an fMRI-Based Targeting Method

Published on: August 2, 2021

3.5K

Related Experiment Videos

Last Updated: Jun 11, 2025

Author Spotlight: Therapeutic Benefit of Closed-Loop Deep Brain Stimulation in Depression Treatment
05:19

Author Spotlight: Therapeutic Benefit of Closed-Loop Deep Brain Stimulation in Depression Treatment

Published on: July 7, 2023

2.2K
Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception
05:48

Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception

Published on: August 9, 2024

1.4K
Individualized rTMS Treatment for Depression using an fMRI-Based Targeting Method
07:12

Individualized rTMS Treatment for Depression using an fMRI-Based Targeting Method

Published on: August 2, 2021

3.5K

Area of Science:

  • Computational linguistics
  • Psychiatry
  • Machine learning

Background:

  • Speech-based depression detection often uses simple classification, overlooking nuanced depression patterns within speech.
  • Existing methods struggle with generalizability due to the non-uniform distribution of depression indicators in speech segments.
  • Lack of fine-grained labels in depression corpora hinders tracking dynamic depression patterns.

Purpose of the Study:

  • To propose a novel framework, Speechformer-CTC, for modeling non-uniformly distributed depression characteristics in speech segments.
  • To address the limitations of conventional speech classification by incorporating a Connectionist Temporal Classification (CTC) objective.
  • To evaluate the framework's compatibility with Automatic Speech Recognition (ASR) features and its performance on diverse datasets.

Main Methods:

  • Developed the Speechformer-CTC framework utilizing a Connectionist Temporal Classification (CTC) objective function.
  • Introduced two novel CTC-label generation policies: Expectation-One-Hot and HuBERT.
  • Integrated the framework with various granularities and tested compatibility with ASR features (HuBERT, Whisper).

Main Results:

  • Achieved improved Macro F1-scores for depression detection on both DAIC-WOZ (English) and CONVERGE (Mandarin) datasets.
  • The system with HuBERT ASR features and HuBERT policy achieved 83.15% F1-score on DAIC-WOZ, nearing state-of-the-art.
  • Significant F1-score improvements were observed on the CONVERGE dataset using Whisper features and the HuBERT policy (9.82% on CONVERGE1, 18.47% on CONVERGE2).

Conclusions:

  • Modeling non-uniformly distributed depression patterns in speech significantly benefits depression detection accuracy.
  • The Speechformer-CTC framework offers a robust and compatible approach, even with standard ASR features.
  • The proposed method can identify significant depressive regions in speech, advancing automatic depression detection systems.