Title:Speech as a Biomarker for Depression
Volume: 22
Issue: 2
Author(s): Sanne Koops*, Sanne G. Brederoo, Janna N. de Boer, Femke G. Nadema, Alban E. Voppel and Iris E. Sommer
Affiliation:
- Department of Biomedical Sciences of Cells & Systems, Cognitive Neurosciences, University of Groningen, University
Medical Center Groningen (UMCG), Groningen, The Netherlands
Keywords:
Computational speech analysis, natural language processing, machine learning, depression, biomarker, categorization, diagnosis.
Abstract:
Background: Depression is a debilitating disorder that at present lacks a reliable biomarker
to aid in diagnosis and early detection. Recent advances in computational analytic approaches
have opened up new avenues in developing such a biomarker by taking advantage of the wealth
of information that can be extracted from a person’s speech.
Objective: The current review provides an overview of the latest findings in the rapidly evolving
field of computational language analysis for the detection of depression. We cover a wide range of
both acoustic and content-related linguistic features, data types (i.e., spoken and written language),
and data sources (i.e., lab settings, social media, and smartphone-based). We put special focus on
the current methodological advances with regard to feature extraction and computational modeling
techniques. Furthermore, we pay attention to potential hurdles in the implementation of automatic
speech analysis.
Conclusion: Depressive speech is characterized by several anomalies, such as lower speech rate,
less pitch variability and more self-referential speech. With current computational modeling techniques,
such features can be used to detect depression with an accuracy of up to 91%. The performance
of the models is optimized when machine learning techniques are implemented that suit the
type and amount of data. Recent studies now work towards further optimization and generalizability
of the computational language models to detect depression. Finally, privacy and ethical issues
are of paramount importance to be addressed when automatic speech analysis techniques are further
implemented in, for example, smartphones. Altogether, computational speech analysis is well
underway towards becoming an effective diagnostic aid for depression.
