Predicting developmental language disorders using artificial intelligence and a speech data analysis tool

ABSTRACT

Developmental Language Disorder (DLD) affects children’s comprehension and production of spoken language without any known biomedical condition. The importance of early identification of DLD is widely acknowledged. Several studies have explored DLD predictors to identify children needing further diagnostic investigation. Most of these measures might be problematic for young children and bilingual children. Based on literature reporting fragile rhythmic abilities in children with DLD, in our study, we followed a different approach. We explored how non-linguistic measures of rhythmic anticipation can be gathered by means of advanced information technology and used to identify children at risk of DLD. With this aim, we developed MARS, a web-based tool to collect such data in a playful way and to analyze them using Machine Learning. MARS engages children in rhythmic babbling exercises, records their vocal productions, and analyzes the recordings. We discuss the methodological rationale of MARS and its underlying technology, and we describe a preliminary study with N = 47 children with and without DLD. The analysis of the audio features of participants’ rhythmic vocal productions highlights different patterns in the two groups. This result, although preliminary, suggests that MARS could be a valuable tool for early DLD assessment.

KEYWORDS:

• Machine learning
• artificial intelligence
• linguistic assessment
• children with DLD
• audio features
• web-application

Acknowledgments

We would like to express our sincere gratitude to all those who have provided support and assistance throughout the course of this research. Without their valuable contributions, this project would not have been possible.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

¹ https://github.com/scikit-learn-contrib/imbalanced-learn

² https://scikit-learn.org/stable/

Additional information

Notes on contributors

Eleonora Aida Beccaluva

Eleonora Aida Beccaluva ([email protected]) is a Psychologist interested in Human-Computer-Interaction, Ux Research, and Innovative Technologies applied to Disability; she is a Ph.D. Candidate in Psychology, Linguistics, and Cognitive Neuroscience at the Psychology Department of Milano-Bicocca University and the Department of Electronics, Information, and Bio-engineering of Politecnico di Milano.

Fabio Catania

Fabio Catania ([email protected]) is a Computer Engineer with an interest in using conversational technology and affective computing to help people with neurodevelopmental disorders enhance communication skills; he is a Postdoctoral Fellow at the McGovern Institute for Brain Research of Massachusetts Institute of Technology and the Department of Electronics, Information and Bio-engineering of Politecnico di Milano.

Fabrizio Arosio

Fabrizio Arosio ([email protected], url) is a Linguist with an interest in temporal information encoding and morphosyntactic information processing; he is a Associate Professor at the Psychology Department of Milano-Bicocca University.

Franca Garzotto

Franca Garzotto ([email protected]) is a Mathematician with an interest in methods and tools for modeling hypermedia applications, usability, and human-machine interaction; she is an Associate Professor in the Department of Electronics, Information, and Bio-engineering of Politecnico di Milano.