Evidence map›Paper›PMID 38865318›Full record

ArticlePloS one2024

The effect of detoxification on acoustic features of Mandarin speech in male heroin users.

Puyang Geng, Ningxue Fan, Zhijun Li, Rong Ling, Kai Yang, Xiao Mao, Hong Guo

Abstract read
In one paragraph

Article in PloS one, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

7 authors.

Puyang GengDepartment of Audio, Video, and Electronic Forensics, Academy of Forensic Science, Shanghai, China.ORCID 0000-0002-5126-9187
Ningxue FanInformation Security and Social Management Innovation Lab, Shanghai Open University, Shanghai, China.
Zhijun LiDepartment of Audio, Video, and Electronic Forensics, Academy of Forensic Science, Shanghai, China.
Rong LingDepartment of Audio, Video, and Electronic Forensics, Academy of Forensic Science, Shanghai, China.
Kai YangDepartment of Audio, Video, and Electronic Forensics, Academy of Forensic Science, Shanghai, China.
Xiao MaoDepartment of Audio, Video, and Electronic Forensics, Academy of Forensic Science, Shanghai, China.
Hong GuoDepartment of Audio, Video, and Electronic Forensics, Academy of Forensic Science, Shanghai, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study aims to investigate the effect of detoxification on acoustic features of Mandarin speech. Speech recordings were collected from 66 male abstinent heroin users with different durations of drug detoxification, specifically early abstinent users with a detoxification duration of less than 2 years, sustained abstinent users with 2 years of detoxification, and long-term abstinent users with a detoxification duration of more than 2 years. The results of the acoustic analyses showed that early abstinent users exhibited lower loudness, relative energies of F1, F2, and F3, higher H1-A3, and fewer loudness peaks per second, as well as a longer average duration of unvoiced segments, compared to the sustained and long-term abstinent users. The findings suggest that detoxification may lead to a rehabilitation process in the speech production of abstinent heroin users (e.g., less vocal hoarseness). This study not only provides valuable insights into the effect of detoxification on speech production but also provides a theoretical basis for the speech rehabilitation and detoxification treatment of heroin users.

Indexed as

Heroin DependenceSpeech AcousticsAdultHumansInactivation, MetabolicLanguageMaleSpeechYoung Adult

Identifiers

PMID38865318
PMCPMC11168614

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.