# Assessing Language Disorders using Artificial Intelligence: a Paradigm Shift

arXiv: https://arxiv.org/abs/2305.20046 · HTML: https://mlsanity.com/paper/2305.20046/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 108 references that are in the map.
- First posted on arXiv 31 May 2023.

## Builds on (most cited first, 12 of 12)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Deep Visual-Semantic Alignments for Generating Image Descriptions](https://mlsanity.com/paper/1412.2306/) (2014)
- [Distributed Representations of Sentences and Documents](https://mlsanity.com/paper/1405.4053/) (2014)
- [Look, Listen and Learn](https://mlsanity.com/paper/1705.08168/) (2017)
- [SoundNet: Learning Sound Representations from Unlabeled Video](https://mlsanity.com/paper/1610.09001/) (2016)
- [Multi-Agent Cooperation and the Emergence of (Natural) Language](https://mlsanity.com/paper/1612.07182/) (2016)
- [Visually Indicated Sounds](https://mlsanity.com/paper/1512.08512/) (2015)
- [CommAI: Evaluating the first steps towards a useful general AI](https://mlsanity.com/paper/1701.08954/) (2017)
- [Sonorant spectra and coarticulation distinguish speakers with different dialects](https://mlsanity.com/paper/2110.03756/) (2021)
- [Modelling prosodic structure using Artificial Neural Networks](https://mlsanity.com/paper/1706.03952/) (2017)

## Abstract (arXiv)

Speech, language, and communication deficits are present in most neurodegenerative syndromes. They enable the early detection, diagnosis, treatment planning, and monitoring of neurocognitive disease progression as part of traditional neurological assessment. Nevertheless, standard speech and language evaluation is time-consuming and resource-intensive for clinicians. We argue that using machine learning methodologies, natural language processing, and modern artificial intelligence (AI) for Language Assessment is an improvement over conventional manual assessment. Using these methodologies, Computational Language Assessment (CLA) accomplishes three goals: (i) provides a neuro-cognitive evaluation of speech, language, and communication in elderly and high-risk individuals for dementia; (ii) facilitates the diagnosis, prognosis, and therapy efficacy in at-risk and language-impaired populations; and (iii) allows easier extensibility to assess patients from a wide range of languages. By employing AI models, CLA may inform neurocognitive theory on the relationship between language symptoms and their neural bases. Finally, it signals a paradigm shift by significantly advancing our ability to optimize the prevention and treatment of elderly individuals with communication disorders, allowing them to age gracefully with social engagement.