Multidisciplinary Perspectives on Automatic Analysis of Children’s Language Samples: Where Do We Go from Here?

Abstract

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, including the tasks of transcribing, annotating and analysing natural child language samples, this article takes on a multi-disciplinary 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.