Computerized Based Analysis for Detection and Severity Assessment of Stuttering

NCT ID: NCT05437627

Last Updated: 2022-06-29

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

UNKNOWN

Total Enrollment

120 participants

Study Classification

OBSERVATIONAL

Study Start Date

2022-09-01

Study Completion Date

2024-10-01

Brief Summary

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In the light of previous attempts to design and develop automated and objective measures for automatic speech recognition system that detects disfluent speech and assess its severity, yet fully automated measurement of stuttered speech is not available. This study was triggered by the need to design and develop a simple and reliable computerized tool for identification of stuttering and measurement for its severity. Therefore, the aim of this study is to develop a user interface that can work on windows system for the adopted stuttering recognition model which can be used in clinical practice by physicians and therapists.

Detailed Description

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Stuttering is a speech disorder in which the normal flow of speech is disrupted by occurrences of dysfluencies, such as repetition, prolongations and blocks (1). Features that have been found to differ between stutterers and nonstutterers are rate of speech and frequency of dysfluent utterances (2).

An Arabic version of stuttering severity instrument (A-SSI) is used to assess the stuttering severity In it, the overall severity score of stuttering is measured by combining the scores of percentages of Stuttered Syllables (%SS), Mean Duration of the Three Longest Stuttering Events (MDTLSE), and Physical Concomitants (PC) (3).

The subjective assessment methods of stuttering are; time-consuming, prone to error, subjective (4), so it is better to automate the measurement of disfluencies using speech recognition technologies and computational intelligence (5).

Speech recognition executes a task similar to what the human brain undertakes (6). Stuttering detection system has three main steps which are acoustic processing, feature extraction and classification/recognition (7). the speech signals are pre-processed (8), and certain features are extracted from them by signal processing techniques, e.g. Mell frequency cepstral coefficients (MFCC) (9). (MFCC) is considered the most popular used feature extraction technique (10).

The classification process contains two steps; training and testing (11). In training process, data is labeled based on the classes and a model is learned. In testing phase: the model is tested and computed the accuracy, sensitivity, and specificity of the classification models (11). Finally, stuttering from non-stuttering speech will be recognized and separated (5) also to assess the severity of stuttered speech.

Conditions

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Stuttering

Study Design

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Observational Model Type

CASE_CONTROL

Study Time Perspective

PROSPECTIVE

Study Groups

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Study Group:

Study Group: This will be consisted of sixty (60) stuttering patients. They will be divided into 2 subgroups; children group 30 patients with age ranges from (10-18y) and adult group 30 patients with age ranges from (19-30y)

The Arabic version of Stuttering Severity Instrument-3 (ASSI3) for children and adults

Intervention Type DIAGNOSTIC_TEST

Assessment of stuttering severity: The Arabic version of Stuttering Severity Instrument-3 (ASSI3) for children and adults will be applied for assessment of severity of stuttering. (3).

Automatic detection and severity assessment of stuttering using MATLAB version 8.1.0.604 R2013a (7). Stuttering detection system has three main steps which are acoustic processing, feature extraction and classification/recognition

Control Group

Control Group: This will be consisted of sixty (60) subjects who have normal fluency. They will be selected from the relative of the patients attending to the outpatient clinic and will be matched for age, sex and socioeconomic state with the patients group.

No interventions assigned to this group

Interventions

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The Arabic version of Stuttering Severity Instrument-3 (ASSI3) for children and adults

Assessment of stuttering severity: The Arabic version of Stuttering Severity Instrument-3 (ASSI3) for children and adults will be applied for assessment of severity of stuttering. (3).

Automatic detection and severity assessment of stuttering using MATLAB version 8.1.0.604 R2013a (7). Stuttering detection system has three main steps which are acoustic processing, feature extraction and classification/recognition

Intervention Type DIAGNOSTIC_TEST

Other Intervention Names

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A. Acoustic voice analysis B. Spectral analysis: Automatic detection and severity assessment of stuttering

Eligibility Criteria

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Inclusion Criteria

1. Age: from 10 to 30 years old.
2. Gender: both sexes will be included in the study.
3. The participants in the study group suffering from developmental stuttering (stuttering symptoms was of early childhood onset, intermittent course and dated since early childhood) seeking speech therapy
4. Have language aptitudes coping with his or her chronological age.

Exclusion Criteria

* 1\. Presence of any other speech or language disorders. 2. Mental Retardation. 3. Poor scholastic performances. 4. Presence of any psychiatric or neurologic disorders.
Minimum Eligible Age

10 Years

Maximum Eligible Age

30 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

No

Sponsors

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Assiut University

OTHER

Sponsor Role lead

Responsible Party

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Asmaa Fathy

principal investigator

Responsibility Role PRINCIPAL_INVESTIGATOR

References

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Bakker K, Brutten GJ. Speech-related reaction times of stutterers and nonstutterers: diagnostic implications. J Speech Hear Disord. 1990 May;55(2):295-9. doi: 10.1044/jshd.5502.295.

Reference Type BACKGROUND
PMID: 2329792 (View on PubMed)

Andrews G, Craig A, Feyer AM, Hoddinott S, Howie P, Neilson M. Stuttering: a review of research findings and theories circa 1982. J Speech Hear Disord. 1983 Aug;48(3):226-46. doi: 10.1044/jshd.4803.226.

Reference Type BACKGROUND
PMID: 6353066 (View on PubMed)

Yairi E. Subtyping stuttering I: a review. J Fluency Disord. 2007;32(3):165-96. doi: 10.1016/j.jfludis.2007.04.001. Epub 2007 Apr 24.

Reference Type BACKGROUND
PMID: 17825668 (View on PubMed)

Prasse JE, Kikano GE. Stuttering: an overview. Am Fam Physician. 2008 May 1;77(9):1271-6.

Reference Type BACKGROUND
PMID: 18540491 (View on PubMed)

SHERMAN D. Clinical and experimental use of the Iowa Scale of Severity of Stuttering. J Speech Hear Disord. 1952 Sep;17(3):316-20. doi: 10.1044/jshd.1703.316. No abstract available.

Reference Type BACKGROUND
PMID: 13053555 (View on PubMed)

O'Brian S, Packman A, Onslow M, O'Brian N. Measurement of stuttering in adults: comparison of stuttering-rate and severity-scaling methods. J Speech Lang Hear Res. 2004 Oct;47(5):1081-7. doi: 10.1044/1092-4388(2004/080).

Reference Type BACKGROUND
PMID: 15603463 (View on PubMed)

Enderby PM, John A. Therapy outcome measures in speech and language therapy: comparing performance between different providers. Int J Lang Commun Disord. 1999 Oct-Dec;34(4):417-29. doi: 10.1080/136828299247360.

Reference Type BACKGROUND
PMID: 10884909 (View on PubMed)

Prins D, Ingham RJ. Evidence-based treatment and stuttering--historical perspective. J Speech Lang Hear Res. 2009 Feb;52(1):254-63. doi: 10.1044/1092-4388(2008/07-0111). Epub 2008 Aug 11.

Reference Type BACKGROUND
PMID: 18695017 (View on PubMed)

Other Identifiers

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Stuttering using Computer

Identifier Type: -

Identifier Source: org_study_id

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