A Privacy-Preserving Unsupervised Speaker Disentanglement Method for Depression Detection from Speech.

Vijay Ravi, Jinhan Wang, Jonathan Flint, Abeer Alwan

Abstract read

In one paragraph

Article in CEUR workshop proceedings, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it

0cells of the map it votes in

1citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

Speechformer-CTC: Sequential Modeling of Depression Detection with Speech Temporal Classification.Speech communication · 2024
Article

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Vijay RaviDepartment of Electrical and Computer Engineering, University of California Los Angeles, California, USA 90095.

Jinhan WangDepartment of Electrical and Computer Engineering, University of California Los Angeles, California, USA 90095.

Jonathan FlintDepartment of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, California, USA 90095.

Abeer AlwanDepartment of Electrical and Computer Engineering, University of California Los Angeles, California, USA 90095.

Funding

Combining Voice and Genetic Information to Detect Heterogeneity in Major Depressive DisorderR01MH122569 · NIMH · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI FLINT, JONATHAN · 2020 to 2024

$3.4M

NIMH NIH HHS R01 MH122569

6 · The paper itself

Abstract

The proposed method focuses on speaker disentanglement in the context of depression detection from speech signals. Previous approaches require patient/speaker labels, encounter instability due to loss maximization, and introduce unnecessary parameters for adversarial domain prediction. In contrast, the proposed unsupervised approach reduces cosine similarity between latent spaces of depression and pre-trained speaker classification models. This method outperforms baseline models, matches or exceeds adversarial methods in performance, and does so without relying on speaker labels or introducing additional model parameters, leading to a reduction in model complexity. The higher the speaker de-identification score (

Indexed as

DAIC-WOZDepression detectionHealthcare AIPrivacySpeaker disentanglement

Identifiers

PMID38650610

PMCPMC11034881

What OpenQuestion holds

Textmetadata

LicenceTDM

Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.