Zobrazeno 1 - 8
of 8
pro vyhledávání: '"Piotr Chlebek"'
Autor:
David Lin, Tahmida Nazreen, Tomasz Rutowski, Yang Lu, Amir Harati, Elizabeth Shriberg, Piotr Chlebek, Michael Aratow
Publikováno v:
Frontiers in Psychology, Vol 13 (2022)
BackgroundDepression and anxiety create a large health burden and increase the risk of premature mortality. Mental health screening is vital, but more sophisticated screening and monitoring methods are needed. The Ellipsis Health App addresses this n
Externí odkaz:
https://doaj.org/article/1294c1e79b6e4b59a5d1ca312d68eaab
Publikováno v:
Interspeech 2022.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::010902956c530e68533f35196f90ab5c
https://doi.org/10.21437/interspeech.2022-10888
https://doi.org/10.21437/interspeech.2022-10888
Autor:
Amir Harati, Tomasz Rutowski, Yang Lu, Piotr Chlebek, Ricardo Oliveira, Elizabeth Shriberg, David Lin
Publikováno v:
Biomedical Sensing and Analysis ISBN: 9783030993825
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::2e7eb0ceec042ad750747ecc7b2f02a6
https://doi.org/10.1007/978-3-030-99383-2_3
https://doi.org/10.1007/978-3-030-99383-2_3
Autor:
Yang Lu, Piotr Chlebek, Amir Harati, Elizabeth Shriberg, Tomasz Rutowski, Ricardo C. L. F. Oliveira
Publikováno v:
ICASSP
Speech-based algorithms have gained interest for the management of behavioral health conditions such as depression. We explore a speech-based transfer learning approach that uses a lightweight encoder and that transfers only the encoder weights, enab
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::32c625c7de0265afca07d9530c8df0eb
https://doi.org/10.1109/icassp39728.2021.9414208
https://doi.org/10.1109/icassp39728.2021.9414208
Autor:
Tomek Rutowski, Amir Harati, Ricardo C. L. F. Oliveira, Yang Lu, Piotr Chlebek, Elizabeth Shriberg
Publikováno v:
SLT
Deep learning models are rapidly gaining interest for real-world applications in behavioral health. An important gap in current literature is how well such models generalize over different populations. We study Natural Language Processing (NLP) based
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::bed6150f5ae4b7c05e71ab7182640f4d
https://doi.org/10.1109/slt48900.2021.9383609
https://doi.org/10.1109/slt48900.2021.9383609
Autor:
Piotr Chlebek, Ricardo C. L. F. Oliveira, Y. Lu, Amir Harati, Tomasz Rutowski, Shriberg Elizabeth E
Publikováno v:
2020 IEEE Signal Processing in Medicine and Biology Symposium (SPMB).
Depression is a global health concern with a critical need for increased patient screening. Speech technology offers advantages for remote screening but must perform robustly across patients. We have described two deep learning models developed for t
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::5864323e1e4c19eb9fec42649ca039d0
https://doi.org/10.1109/spmb50085.2020.9353611
https://doi.org/10.1109/spmb50085.2020.9353611
Autor:
Elizabeth Shriberg, Ricardo C. L. F. Oliveira, Yang Lu, Amir Harati, Piotr Chlebek, Tomasz Rutowski
Publikováno v:
BESC
Digital screening and monitoring applications can aid providers in the management of behavioral health conditions. We explore deep language models for detecting depression, anxiety, and their comorbidity using input from conversational speech. Speech
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a549205f72b63b76eb5672d61364a5e6
https://doi.org/10.1109/besc51023.2020.9348290
https://doi.org/10.1109/besc51023.2020.9348290
Autor:
Tomasz Rutowski, Yang Lu, Ricardo C. L. F. Oliveira, Piotr Chlebek, Amir Harati, Shriberg Elizabeth E
Publikováno v:
UbiComp/ISWC Adjunct
Behavioral health conditions such as depression and anxiety are a global concern, and there is growing interest in employing speech technology to screen and monitor patients remotely. Language modeling approaches require automatic speech recognition
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::73a91dad0ee1852936e20ee2af5d07c8
https://doi.org/10.1145/3410530.3414372
https://doi.org/10.1145/3410530.3414372