Phonologically-based biomarkers for major depressive disorder

Andrea Carolina Trevino,Nicolas Malyska,Thomas Francis Quatieri

doi:10.1186/1687-6180-2011-42

Andrea Carolina Trevino, Nicolas Malyska + Show 1 more

Open Access

https://doi.org/10.1186/1687-6180-2011-42

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Abstract

Abstract Of increasing importance in the civilian and military population is the recognition of major depressive disorder at its earliest stages and intervention before the onset of severe symptoms. Toward the goal of more effective monitoring of depression severity, we introduce vocal biomarkers that are derived automatically from phonologically-based measures of speech rate. To assess our measures, we use a 35-speaker free-response speech database of subjects treated for depression over a 6-week duration. We find that dissecting average measures of speech rate into phone-specific characteristics and, in particular, combined phone-duration measures uncovers stronger relationships between speech rate and depression severity than global measures previously reported for a speech-rate biomarker. Results of this study are supported by correlation of our measures with depression severity and classification of depression state with these vocal measures. Our approach provides a general framework for analyzing individual symptom categories through phonological units, and supports the premise that speaking rate can be an indicator of psychomotor retardation severity.

Highlights

Major depressive disorder (MDD) is the most widely affecting of the mood disorders; the lifetime risk has been observed to fall between 10 and 20% and 5 and 12% for women and men, respectively [1]
The specific focus in this article is on biomarkers derived from speech rate, we provide a general framework in which to explore the relationship between phonologically-based biomarkers and the severity of individual MDD symptoms
We examine the correlations of the HAMD ratings with the average duration of pauses and automatic recognition-based individual English phone durations, providing a fine-grained analysis of speech timing

Summary

Introduction

Major depressive disorder (MDD) is the most widely affecting of the mood disorders; the lifetime risk has been observed to fall between 10 and 20% and 5 and 12% for women and men, respectively [1]. We examine the correlations of the HAMD ratings with the average duration of pauses and automatic recognition-based individual English phone durations, providing a fine-grained analysis of speech timing. The individual phone durations that show significant correlations within a single HAMD category (total or sub-topic) are observed to cluster approximately within manner-of-articulation categories and according to the strength of intercorrelation between sub-topics. Our results provide the framework for a phone-specific approach in the study of vocal biomarkers for depression, as well as for analyzing individual symptom categories. To further exploit this framework, the scarcity and variability of samples in our database points to a need for further experiments with larger populations to account for the variety within one group of MDD patients.

Background and previous studies

Conclusions and ongoing study

Findings

Insomnia