TY - GEN
T1 - Ecologically valid long-term mood monitoring of individuals with bipolar disorder using speech
AU - Karam, Zahi N.
AU - Provost, Emily Mower
AU - Singh, Satinder
AU - Montgomery, Jennifer
AU - Archer, Christopher
AU - Harrington, Gloria
AU - McInnis, Melvin G.
PY - 2014
Y1 - 2014
N2 - Speech patterns are modulated by the emotional and neurophysiological state of the speaker. There exists a growing body of work that computationally examines this modulation in patients suffering from depression, autism, and post-traumatic stress disorder. However, the majority of the work in this area focuses on the analysis of structured speech collected in controlled environments. Here we expand on the existing literature by examining bipolar disorder (BP). BP is characterized by mood transitions, varying from a healthy euthymic state to states characterized by mania or depression. The speech patterns associated with these mood states provide a unique opportunity to study the modulations characteristic of mood variation. We describe methodology to collect unstructured speech continuously and unobtrusively via the recording of day-to-day cellular phone conversations. Our pilot investigation suggests that manic and depressive mood states can be recognized from this speech data, providing new insight into the feasibility of unobtrusive, unstructured, and continuous speech-based wellness monitoring for individuals with BP.
AB - Speech patterns are modulated by the emotional and neurophysiological state of the speaker. There exists a growing body of work that computationally examines this modulation in patients suffering from depression, autism, and post-traumatic stress disorder. However, the majority of the work in this area focuses on the analysis of structured speech collected in controlled environments. Here we expand on the existing literature by examining bipolar disorder (BP). BP is characterized by mood transitions, varying from a healthy euthymic state to states characterized by mania or depression. The speech patterns associated with these mood states provide a unique opportunity to study the modulations characteristic of mood variation. We describe methodology to collect unstructured speech continuously and unobtrusively via the recording of day-to-day cellular phone conversations. Our pilot investigation suggests that manic and depressive mood states can be recognized from this speech data, providing new insight into the feasibility of unobtrusive, unstructured, and continuous speech-based wellness monitoring for individuals with BP.
KW - Bipolar Disorder
KW - Speech Analysis
KW - mood modeling
UR - http://www.scopus.com/inward/record.url?scp=84905284205&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84905284205&partnerID=8YFLogxK
U2 - 10.1109/ICASSP.2014.6854525
DO - 10.1109/ICASSP.2014.6854525
M3 - Conference contribution
C2 - 27630535
AN - SCOPUS:84905284205
SN - 9781479928927
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
SP - 4858
EP - 4862
BT - 2014 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2014
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2014 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2014
Y2 - 4 May 2014 through 9 May 2014
ER -