Computerized Based Analysis for Detection and Severity Assessment of Stuttering

Assiut University

Status: Not yet enrolling

Conditions: Stuttering

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

Study type: Observational

Funder types: Other

Identifiers

NCT05437627

Stuttering using Computer

Details and patient eligibility

About

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.

Full description

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.

Enrollment: 120 estimated patients

Sex: All

Ages: 10 to 30 years old

Volunteers: No Healthy Volunteers

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.

Trial design

120 participants in 2 patient groups

Study Group:

Description:

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)

Treatment:

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

Control Group

Description:

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.