Multidisciplinary perspectives on automatic analysis of children's language samples : where do we go from here?

BACKGROUND : Language sample analysis (LSA) is invaluable to describe and understand child language use and development for clinical purposes and research. Digital tools supporting LSA are available, but many of the LSA steps have not been automated. Nevertheless, programs that include automatic speech recognition (ASR), the first step of LSA, have already reached mainstream applicability. SUMMARY : To better understand the complexity, challenges, and future needs of automatic LSA from a technological perspective, including the tasks of transcribing, annotating, and analysing natural child language samples, this article takes on a multidisciplinary view. Requirements of a fully automated LSA process are characterized, features of existing LSA software tools compared, and prior work from the disciplines of information science and computational linguistics reviewed. KEY MESSAGES : Existing tools vary in their extent of automation provided across the process of LSA. Advances in machine learning for speech recognition and processing have potential to facilitate LSA, but the specifics of child speech and language as well as the lack of child data complicate software design. A transdisciplinary approach is recommended as feasible to support future software development for LSA.https://karger.com/fplhj2023Centre for Augmentative and Alternative Communication (CAAC)Speech-Language Pathology and Audiolog

Research on the methodology of LSA with preschool children : a scoping review

PURPOSE : Language Sample Analysis (LSA) is a prominent method in researching language development and is also used in clinical practice in the speech-language pathology (SLP) discipline. This scoping review aims to describe current contributions of research on LSA methodology, identify research gaps and explore areas of future advancement of LSA methodology related to its five components: determining the sample length/size, collecting, transcribing, coding and analyzing the sample. METHODS : A scoping review was conducted of studies on LSA methodology published between 2010–2020 that focused on preschool children. Relevant electronic databases and research platforms were searched using the PRISMA method for data identification, screening, selection and extraction. RESULTS : Of the 213 identified studies, 61 met the inclusion criteria, covering all aspects of the LSA process. Overall, a wide variability in study designs and research foci were found, reflecting the broad applicability of LSA. The two LSA aspects addressed most frequently are the first and last of the five LSA components: determining the length (or size) of the language sample and analyzing the sample. The methodological variability hinders the comparison of evidence and drawing implications which negatively impacts on research and clinical SLP practice. CONCLUSIONS : Besides expanding research on LSA for multilingual children and establishing LSA guidelines for specific contexts, age groups and language backgrounds, it appears as if technological development, particularly in the (semi)automatic transcription, coding and analysis of child language, holds promise to improve LSA applicability and efficiency.https://www.e-cacd.orgam2024Centre for Augmentative and Alternative Communication (CAAC)SDG-03:Good heatlh and well-bein