TY - CHAP
T1 - Speech and the n-Back task as a lens into depression. How combining both may allow us to isolate different core symptoms of depression
AU - Fara, Salvatore
AU - Goria, Stefano
AU - Molimpakis, Emilia
AU - Cummins, Nicholas
N1 - Publisher Copyright:
Copyright © 2022 ISCA.
PY - 2022/9/18
Y1 - 2022/9/18
N2 - Embedded in any speech signal is a rich combination of cognitive, neuromuscular and physiological information. This richness makes speech a powerful signal in relation to a range of different health conditions, including major depressive disorders (MDD). One pivotal issue in speech-depression research is the assumption that depressive severity is the dominant measurable effect. However, given the heterogeneous clinical profile of MDD, it may actually be the case that speech alterations are more strongly associated with subsets of key depression symptoms. This paper presents strong evidence in support of this argument. First, we present a novel large, cross-sectional, multimodal dataset collected at Thymia. We then present a set of machine learning experiments that demonstrate that combining speech with features from an n-Back working memory assessment improves classifier performance when predicting the popular eight-item Patient Health Questionnaire depression scale (PHQ-8). Finally, we present a set of experiments that highlight the association between different speech and n-Back markers at the PHQ-8 item level. Specifically, we observe that somatic and psychomotor symptoms are more strongly associated with n-Back performance scores, whilst the other items: anhedonia, depressed mood, change in appetite, feelings of worthlessness and trouble concentrating are more strongly associated with speech changes.
AB - Embedded in any speech signal is a rich combination of cognitive, neuromuscular and physiological information. This richness makes speech a powerful signal in relation to a range of different health conditions, including major depressive disorders (MDD). One pivotal issue in speech-depression research is the assumption that depressive severity is the dominant measurable effect. However, given the heterogeneous clinical profile of MDD, it may actually be the case that speech alterations are more strongly associated with subsets of key depression symptoms. This paper presents strong evidence in support of this argument. First, we present a novel large, cross-sectional, multimodal dataset collected at Thymia. We then present a set of machine learning experiments that demonstrate that combining speech with features from an n-Back working memory assessment improves classifier performance when predicting the popular eight-item Patient Health Questionnaire depression scale (PHQ-8). Finally, we present a set of experiments that highlight the association between different speech and n-Back markers at the PHQ-8 item level. Specifically, we observe that somatic and psychomotor symptoms are more strongly associated with n-Back performance scores, whilst the other items: anhedonia, depressed mood, change in appetite, feelings of worthlessness and trouble concentrating are more strongly associated with speech changes.
KW - cognitive games
KW - computational paralinguistics
KW - depression
KW - n-Back
KW - symptom measurements
UR - http://www.scopus.com/inward/record.url?scp=85140086668&partnerID=8YFLogxK
U2 - 10.21437/Interspeech.2022-10393
DO - 10.21437/Interspeech.2022-10393
M3 - Conference paper
AN - SCOPUS:85140086668
T3 - Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH
SP - 1911
EP - 1915
BT - Proc. Interspeech 2022
T2 - 23rd Annual Conference of the International Speech Communication Association, INTERSPEECH 2022
Y2 - 18 September 2022 through 22 September 2022
ER -