Date of Graduation

5-2025

Document Type

Thesis

Degree Name

Bachelor of Science in Communication Sciences and Disorders

Degree Level

Undergraduate

Department

Communication Disorders and Occupational Therapy

Advisor/Mentor

Bowers, Lisa

Committee Member

Glade, Rachel

Abstract

Introduction: Language sample analysis is a highly beneficial tool that speech-language pathologists can utilize to provide a more accurate diagnosis and treatment plan for their client. This method is particularly valuable because it assesses a child’s speech in a naturalistic form, allowing therapists to observe how the child communicates in real-world settings. Speech sound disorders account for the highest number of cases treated by speech-language pathologist in the pediatric population. This thesis aims to investigate two different types of language sample analysis software, CLAN and SALT, as well as their benefits and limitations in diagnosing speech sound disorders. Understanding the use, strengths, and weaknesses of these programs could help speech-language pathologists make more informed decisions, leading to individualization of treatment methods and ultimately resulting in more effective treatment outcomes for children with speech sound disorders.

Methods: The research and data collection for this thesis were conducted at Old Farmington Road HeadStart Center in Fayetteville, Arkansas. Data were collected from a single participant over four months. The participant was a four-year-old female suspected of having an articulation disorder. Open-ended questions were used during a conversation-based session between the participant and examiner. The language sample was then transcribed using both SALT and CLAN databases. The transcription, coding, and analysis procedures to the time spent using each software. The results from the software analyses were then examined to aid in determining a diagnosis and therapy plan for the participant.

Results: Both language sampling software’s produced similar results for the child with a potential speech sound disorder. It was shown that her Type Token Ratio (TTR) and repetition of words were above average, while mean length of utterances (MLU) in words and intelligible words were below average. However, her number of total words (NTW) and response to questions remained higher than others her same age.

Conclusion: Overall, the findings indicate that language sample analysis is a highly beneficial tool for influencing effective diagnosis and therapy plans for speech sound disorders. The naturalistic setting allows therapists to observe how children communicate in real-world situations and identify areas of difficulty. However, its practical use for speech-language pathologist remains a consideration. Both software programs were found to be challenging to learn and master in a short period of time. Additionally, the time required to generate an analysis is not practical for a speech-language pathologist managing large case load. It was concluded that future studies should investigate the integration of accurate speech-to-text functionality and Artificial Intelligence (AI) to reduce the time required for transcription and coding.

Keywords

Language Sample Analysis; CLAN; SALT; Speech Sound Disorders; Norm-Referenced Assessment

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