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

Research output: Contribution to journalReview articlepeer-review

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.

Original languageEnglish (US)
Article number8894069
Pages (from-to)282-298
Number of pages17
JournalIEEE Journal on Selected Topics in Signal Processing
Volume14
Issue number2
DOIs
StatePublished - Feb 2020

Keywords

  • Alzheimer's disease
  • Cognitive linguistics
  • natural language processing
  • schizophrenia
  • thought disorders
  • vocal biomarkers

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering

Fingerprint Dive into the research topics of 'A Review of Automated Speech and Language Features for Assessment of Cognitive and Thought Disorders'. Together they form a unique fingerprint.

Cite this