Comutti - A Research Project Dedicated to Finding Smart Ways of Using Technology for a Better Tomorrow for Everyone, Everywhere.

Summary

According to World Health Organization, worldwide one in 160 children has an ASD. About around 25% to 30% of children are unable to use verbal language to communicate (non-verbal ASD) or are minimally verbal, i.e., use fewer than 10 words (mv-ASD). The ability to communicate is a crucial life skill, and difficulties with communication can have a range of negative consequences such as poorer quality of life and behavioural difficulties. Communication interventions generally aim to improve children's ability to communicate either through speech or by supplementing speech with other means (e.g., sign language, pictures, or AAC - Advanced Augmented Communication tools). Individuals with non- verbal ASD or mv-ASD often communicate with people through vocalizations that in some cases have a self-consistent phonetic association to concepts (e.g., "ba" to mean "bathroom") or are onomatopoeic expressions (e.g., "woof" to refer to a dog). In most cases vocalizations sound arbitrary; even if they vary in tone, pitch, and duration depending it is extremely difficult to interpret the intended message or the individual's emotional or physical state they would convey, creating a barrier between the persons with ASD and the rest of the world that originate stress and frustration. Only caregivers who have long term acquaintance with the subjects are able to decode such wordless sounds and assign them to unique meanings.

This project aims at defining algorithms, methods, and technologies to identify the communicative intent of vocal expressions generated by children with mv-ASD, and to create tools that help people who are not familiar with the subjects to understand these individuals during spontaneous conversations.

Conditions

• Autism Spectrum Disorder

Interventions

DIAGNOSTIC_TEST

Clinical evaluation of participants by means of Autism Diagnostic Observation Schedule

Clinical evaluation of participants by means of Autism Diagnostic Observation Schedule

BEHAVIORAL

audio signal dataset creation and validation; machine learning analysis, empirical evaluations

The project tests and adapts the technology developed at MIT for vocalization collection and labeling, and contributes to data gathering among Italian subjects (and their quality validation) in order to create a multi-cultural dataset and to enable cross-cultural studies and analyses. Next, the focus is placed on the analysis of harmonic features of the audio in the vocalizations of the dataset to identify recurring individual features and patterns corresponding to specific communications purposes or emotional states. Supervised and unsupervised machine learning approaches are developed and different machine learning algorithms will be compared to identify the most accurate ones for the project goal. Last, an exploratory evaluation of the vocalization-understanding machine learning model is conducted to test the usability and utility of the tool for vocalization interpretation.

Sponsors & Collaborators

• Politecnico di Milano

  collaborator OTHER

• Massachusetts Institute of Technology

  collaborator OTHER

• IRCCS Eugenio Medea

  lead OTHER

Principal Investigators

• Alessandro Crippa, Ph.D. · IRCCS Eugenio Medea

Study Design

Allocation

NA

Purpose

BASIC_SCIENCE

Masking

NONE

Model

SINGLE_GROUP

Eligibility

Min Age

2 Years

Max Age

10 Years

Sex

ALL

Healthy Volunteers

No

Timeline & Regulatory

Start

2021-07-27

Primary Completion

2024-12-31

Completion

2024-12-31

Countries

• Italy

Study Locations