  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.
