An observational study in Stuttering, sponsored by Assiut University. Status unknown. Open to participants aged 10 Years to 30 Years. Per ClinicalTrials.gov, last updated 2022-06-29.
Sponsored by Assiut University · Observational
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.
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.
74 studies on the registry are indexed under Stuttering; 23 are open to participants now.
This study's planned enrollment of 120 is above the median of 60 across 15 observational studies indexed under Stuttering.
Browse Stuttering studies →Assiut University is the lead sponsor of 5,078 studies on the registry; 2,178 are open to participants now.
Of its 14 completed or terminated interventional studies of FDA-regulated products, 0 (0%) have results posted.
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The study will be included one hundred twenty (120) subjects; age ranges (10-30 years). They will be recruited from the outpatient clinic of Phoniatric Unit, Assiut University Hospital. The study is supposed to be conducted in one up to 2 years. They will be divided into two groups:
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Exclusion Criteria:
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)
Diagnostic Test: The Arabic version of Stuttering Severity Instrument-3 (ASSI3) for children and adults
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.
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
Also known as: A. Acoustic voice analysis, B. Spectral analysis:, Automatic detection and severity assessment of stuttering
Assessment of stuttering severity:
using both subjective method as The Arabic version of Stuttering Severity Instrument-3 (ASSI3) for children and adults and objective method asAutomatic detection and severity assessment of stuttering
Time frame: baseline
No study locations are listed for this record.
Start is estimated for Sep 2022; primary completion for Sep 2024, 24 months later.
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This study is status unknown, as verified in Jun 2022. You cannot join it, but the record below documents what was studied.
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