# A Review of Automated Speech and Language Features for Assessment of Cognitive and Thought Disorders

arXiv: https://arxiv.org/abs/1906.01157 · HTML: https://mlsanity.com/paper/1906.01157/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Jun 2019.

## Built on by (most cited first, 11 of 11)

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- [Reformulating NLP tasks to Capture Longitudinal Manifestation of Language Disorders in People with Dementia](https://mlsanity.com/paper/2310.09897/) (2023)
- [A Systematic Review of NLP for Dementia -- Tasks, Datasets and Opportunities](https://mlsanity.com/paper/2409.19737/) (2024)
- [A Longitudinal Multi-modal Dataset for Dementia Monitoring and Diagnosis](https://mlsanity.com/paper/2109.01537/) (2021)
- [The Sound of Syntax: Finetuning and Comprehensive Evaluation of Language Models for Speech Pathology](https://mlsanity.com/paper/2509.16765/) (2025)
- [Explainable cognitive decline detection in free dialogues with a Machine Learning approach based on pre-trained Large Language Models](https://mlsanity.com/paper/2411.02036/) (2024)
- [The order in speech disorder: a scoping review of state of the art machine learning methods for clinical speech classification](https://mlsanity.com/paper/2503.04802/) (2025)
- [Beyond Prompt: Fine-grained Simulation of Cognitively Impaired Standardized Patients via Stochastic Steering](https://mlsanity.com/paper/2604.12210/) (2026)
- [Automated Clinical Report Generation for Remote Cognitive Remediation: Comparing Knowledge-Engineered Templates and LLMs in Low-Resource Settings](https://mlsanity.com/paper/2605.06594/) (2026)
- [Exploration of Perceptual Speech Features for Clinical Decision-Support in Mental Health Care](https://mlsanity.com/paper/2605.24678/) (2026)
- [LLM-Anchored Paralinguistic Enrichment for Alzheimer's Disease Detection](https://mlsanity.com/paper/2609.10896/) (2026)

## Abstract (arXiv)

It is widely accepted that information derived from analyzing speech (the acoustic signal) and language production (words and sentences) serves as a useful window into the health of an individual's cognitive ability. In fact, most neuropsychological testing batteries have a component related to speech and language where clinicians elicit speech from patients for subjective evaluation across a broad set of dimensions. With advances in speech signal processing and natural language processing, there has been recent interest in developing tools to detect more subtle changes in cognitive-linguistic function. This work relies on extracting a set of features from recorded and transcribed speech for objective assessments of speech and language, early diagnosis of neurological disease, and tracking of disease after diagnosis. With an emphasis on cognitive and thought disorders, in this paper we provide a review of existing speech and language features used in this domain, discuss their clinical application, and highlight their advantages and disadvantages. Broadly speaking, the review is split into two categories: language features based on natural language processing and speech features based on speech signal processing. Within each category, we consider features that aim to measure complementary dimensions of cognitive-linguistics, including language diversity, syntactic complexity, semantic coherence, and timing. We conclude the review with a proposal of new research directions to further advance the field.