Evidence map›Paper›PMID 35009610›Full record

ArticleSensors (Basel, Switzerland)2021

Performance Evaluation of a Voice-Based Depression Assessment System Considering the Number and Type of Input Utterances.

Masakazu Higuchi, Noriaki Sonota, Mitsuteru Nakamura, Kenji Miyazaki, Shuji Shinohara, Yasuhiro Omiya, Takeshi Takano, Shunji Mitsuyoshi, Shinichi Tokuno

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. 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

9 authors.

Masakazu HiguchiDepartment of Bioengineering, Graduate School of Engineering, The University of Tokyo, Tokyo 113-8656, Japan.ORCID 0000-0003-0329-1141
Noriaki SonotaDepartment of Bioengineering, Graduate School of Engineering, The University of Tokyo, Tokyo 113-8656, Japan.
Mitsuteru NakamuraDepartment of Bioengineering, Graduate School of Engineering, The University of Tokyo, Tokyo 113-8656, Japan.ORCID 0000-0002-5851-5424
Kenji MiyazakiMitsui Knowledge Industry Co., Ltd., Tokyo 105-6215, Japan.ORCID 0000-0002-1647-3190
Shuji ShinoharaDepartment of Bioengineering, Graduate School of Engineering, The University of Tokyo, Tokyo 113-8656, Japan.ORCID 0000-0001-8442-836X
Yasuhiro OmiyaPST Inc., Kanagawa 231-0023, Japan.ORCID 0000-0003-3343-0368
Takeshi TakanoPST Inc., Kanagawa 231-0023, Japan.ORCID 0000-0002-0669-0175
Shunji MitsuyoshiDepartment of Bioengineering, Graduate School of Engineering, The University of Tokyo, Tokyo 113-8656, Japan.ORCID 0000-0002-3441-3335
Shinichi TokunoDepartment of Bioengineering, Graduate School of Engineering, The University of Tokyo, Tokyo 113-8656, Japan.ORCID 0000-0002-2691-6979

Funding

JSPS KAKENHI Grant 17K01404
6 · The paper itself

Abstract

It is empirically known that mood changes affect facial expressions and voices. In this study, the authors have focused on the voice to develop a method for estimating depression in individuals from their voices. A short input voice is ideal for applying the proposed method to a wide range of applications. Therefore, we evaluated this method using multiple input utterances while assuming a unit utterance input. The experimental results revealed that depressive states could be estimated with sufficient accuracy using the smallest number of utterances when positive utterances were included in three to four input utterances.

Indexed as

DepressionVoiceHumansmental healthmood change by positive utterancesvoice biomarker

Identifiers

PMID35009610
PMCPMC8747535

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.