CLSep 13, 2024

Towards Precision Characterization of Communication Disorders using Models of Perceived Pragmatic Similarity

arXiv:2409.09170v1h-index: 5
Originality Synthesis-oriented
AI Analysis

This work addresses the challenge of improving diagnosis and treatment for individuals with communication disorders, but it appears incremental as it builds on existing speech technology with a new model application.

The paper tackled the problem of characterizing communication disorders by proposing a general-purpose model of perceived pragmatic similarity to address diversity of conditions, pragmatic deficits, and limited data, with evidence showing it can capture utterance aspects relevant to diagnoses of autism and specific language impairment.

The diagnosis and treatment of individuals with communication disorders offers many opportunities for the application of speech technology, but research so far has not adequately considered: the diversity of conditions, the role of pragmatic deficits, and the challenges of limited data. This paper explores how a general-purpose model of perceived pragmatic similarity may overcome these limitations. It explains how it might support several use cases for clinicians and clients, and presents evidence that a simple model can provide value, and in particular can capture utterance aspects that are relevant to diagnoses of autism and specific language impairment.

Foundations

The foundational work for this paper's niche, ranked by how specifically the neighbourhood builds on it — not by global fame.

Your Notes