1. DETECTING STRESS AND DEPRESSION IN ADULTS WITH APHASIA THROUGH SPEECH ANALYSIS
Stephanie Gillespie1, Elliot Moore1, Jacqueline Laures-Gore2, Matthew Farina2 , Scott Russell3, Yash-Yee Logan1
1 Georgia Institute of Technology, 2 Georgia State University, 3 Grady Memorial Hospital
{sgillespie6, em80,
Background
Results
Aphasia is an acquired communication disorder resulting from brain damage and impairs an individual’s ability to use, produce, and comprehend language. Loss of communication skills can be stressful and may result in depression, yet most stress and depression diagnostic tools are designed for adults without aphasia. This project is a research effort to predict stress and depression from acoustic profiles of adults with aphasia using linear support-vector regression. The labels were obtained through caregiver surveys (SADQ-10) or surveys not designed for adults with aphasia (PSS).
SADQ-10 Depression Prediction Results
PSS Stress Prediction
Results
Feature Type
MAE
(SADQ-σ) R2
P1SD
(%)
(PSS-σ)
All
1.24
0.04
46.0%
1.51
0.12
36.8%
Pitch + Jitter
1.08
0.34
48.4%
1.05
0.18
54.4%
RMS-Energy
0.05
52.3%
0.94
61.4%
LSF + Δ
1.11
0.07
53.0%
1.17
0.11
43.9%
MFCC + Δ
1.25
47.7%
1.33
HNR
0.97
0.15
57.5%
1.02
55.8%
CPP
1.03
0.13
54.7%
TEO-All
1.15
0.03
1.01
0.00
55.3%
TEO-AM
1.04
56.8%
0.29
61.1%
TEO-FM
0.91
62.1%
1.06
0.25
53.2%
TEO-CBarea
53.7%
55.0%
TEO-RMS Energy
54.0%
0.40
56.1%
TEO-log Energy
0.14
50.2%
0.98
0.09
59.4%
Glottal-All
1.16
49.5%
0.02
52.9%
H1-H2
PSP
0.44
51.9%
0.24
HRF
1.07
0.17
0.89
59.9%
GLTP
1.21
44.9%
1.00
0.01
53.5%
Data Collection
Full Recording Material:
Speech components of the Western Aphasia Battery-Revised (WAB-R) Protocol
2 additional Picture Descriptions
Participants Included:
19 Participants selected for depression analysis based on Stroke Aphasia Depression Questionaire-10 (SADQ-10)
18 participants selected for stress analysis based on Perceived Stress Scale (PSS)
Speech Analyzed:
Used only phrase or sentence responses- no single words
Spontaneous responses only- no repeat-after-me or fill in the blank responses.
18 sentences per person
Pictures used to elicit spontaneous speech (from left to right): Picnic Scene included in the WAB, Cat in Tree photo, and Cookie Theft photo from the BDAE.
Sample PSS questions (participant):
Sample SADQ-10 questions (caregiver):
-In the last month, how often have you been able to control irritations in your life?
-Does he/she avoid eye contact when you talk to him/her?
-In the last month, how often have you felt nervous and “stressed”?
-Does he/she sit without doing anything?
Participant characteristics that may influence speech acoustics
Participant characteristics included in this study
Age
Gender
Aphasia Type (WAB-R)
Aphasia Quotient (WAB-R)
Dysarthria (FDA-2)
Apraxia (ABA-2)
Mood
# Males
12*
# Females 7
Age Range
31-70 AQ
# with AQ>93.8 2
SADQ-10 score
6-25
PSS score
14-40
SADQ-10 and PSS regression results by feature subtype after feature selection.
MAE= Mean Absolute Error, units are with respect to score-σ
R2=R-Squared Score
P1SD= Percentage of predictions within one score-σ from the actual value
Depression (SADQ-10)
Stress (PSS)
Discussion
Feature Extraction and Regression
Correlation analysis between prediction median, mean, standard deviation, IQR, and accuracy and clinical/demographic information found no statistically-significant correlations
Models do not appear to perform better or worse based on any specific clinical or demographic characteristics of the individuals
Linear-SVR may not be able to handle complexities of the data, or possibly the database is too limited for generalizations of stress/depression to be drawn
Future work will investigate:
Emotional state/mood of the participants
Impact of motor disorders on speech from adults with aphasia as it relates to affect
Multi-session interview and recording data collection
Pre-Processing
Segment recordings into individual responses
Voiced Speech Detection
Results of prior work indicate poor classification performance on participants with scores just above/below threshold for binary depression label. Regression avoids the boundary errors by predicting the depression score instead of classifying.
Feature Extraction
Prosodic
Spectral
Teager Energy Operator
Glottal
Experiment Setup in Matlab
Feature Selection
Remove correlations >0.75, then 10-fold cross validation SFS
Leave-one-participant-out train/test sets
Linear Support-Vector regression model built and predictions observed
Acknowledgements
Supported by the Emory-Georgia Institute of Technology Healthcare Innovation Program and the National Center for Advancing Translational Sciences of the National Institutes of Health under Award Number UL1TR The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. Work supported by the National Science Foundation Graduate Research Fellowship, Grant No. DGE
SADQ-10 range: 0-30
PSS range: 0-56