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

## TL;DR

This review summarizes speech and language features used for assessing cognitive and thought disorders, highlighting their clinical applications, advantages, disadvantages, and proposing future research directions.

## Contribution

It provides a comprehensive overview of existing speech and language features for cognitive assessment, emphasizing recent advances and identifying gaps for future exploration.

## Key findings

- Speech and language features can detect subtle cognitive changes.
- Natural language processing and speech signal processing are key tools.
- Features vary in effectiveness for different disorders.

## Abstract

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.