Paper Info
Title
A Review of Automated Speech and Language Features for Assessment of Cognitive and Thought 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.
Year
DOI
Venue
2020
10.1109/JSTSP.2019.2952087
IEEE Journal of Selected Topics in Signal Processing
Keywords
DocType
Volume
Feature extraction,Speech processing,Natural language processing,Dementia,Complexity theory
Journal
14
Issue
ISSN
Citations 
2
1932-4553
0
PageRank 
References 
Authors
0.34
0
3
Name
Order
Citations
PageRank
Rohit Voleti121.72
Julie Liss2105.98
Visar Berisha37622.38