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Showing 1 to 12 of 17 entries
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Designing Contestability: Interaction Design, Machine Learning, and Mental Health.

DIS. Designing Interactive Systems (Conference)

Hirsch T, Merced K, Narayanan S, Imel ZE, Atkins DC.
PMID: 28890949
DIS (Des Interact Syst Conf). 2017 Jun;2017:95-99. doi: 10.1145/3064663.3064703.

We describe the design of an automated assessment and training tool for psychotherapists to illustrate challenges with creating interactive machine learning (ML) systems, particularly in contexts where human life, livelihood, and wellbeing are at stake. We explore how existing...

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Hirsch T, Merced K, Narayanan S, et al. Designing Contestability: Interaction Design, Machine Learning, and Mental Health. DIS (Des Interact Syst Conf). 2017;2017:95-99doi: 10.1145/3064663.3064703.
Hirsch, T., Merced, K., Narayanan, S., Imel, Z. E., & Atkins, D. C. (2017). Designing Contestability: Interaction Design, Machine Learning, and Mental Health. DIS. Designing Interactive Systems (Conference), 201795-99. https://doi.org/10.1145/3064663.3064703
Hirsch, Tad, et al. "Designing Contestability: Interaction Design, Machine Learning, and Mental Health." DIS. Designing Interactive Systems (Conference) vol. 2017 (2017): 95-99. doi: https://doi.org/10.1145/3064663.3064703
Hirsch T, Merced K, Narayanan S, Imel ZE, Atkins DC. Designing Contestability: Interaction Design, Machine Learning, and Mental Health. DIS (Des Interact Syst Conf). 2017 Jun;2017:95-99. doi: 10.1145/3064663.3064703. PMID: 28890949; PMCID: PMC5590649.
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Computational Analysis and Simulation of Empathic Behaviors: a Survey of Empathy Modeling with Behavioral Signal Processing Framework.

Current psychiatry reports

Xiao B, Imel ZE, Georgiou P, Atkins DC, Narayanan SS.
PMID: 27017830
Curr Psychiatry Rep. 2016 May;18(5):49. doi: 10.1007/s11920-016-0682-5.

Empathy is an important psychological process that facilitates human communication and interaction. Enhancement of empathy has profound significance in a range of applications. In this paper, we review emerging directions of research on computational analysis of empathy expression and...

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Xiao B, Imel ZE, Georgiou P, et al. Computational Analysis and Simulation of Empathic Behaviors: a Survey of Empathy Modeling with Behavioral Signal Processing Framework. Curr Psychiatry Rep. 2016;18(5):49doi: 10.1007/s11920-016-0682-5.
Xiao, B., Imel, Z. E., Georgiou, P., Atkins, D. C., & Narayanan, S. S. (2016). Computational Analysis and Simulation of Empathic Behaviors: a Survey of Empathy Modeling with Behavioral Signal Processing Framework. Current psychiatry reports, 18(5), 49. https://doi.org/10.1007/s11920-016-0682-5
Xiao, Bo, et al. "Computational Analysis and Simulation of Empathic Behaviors: a Survey of Empathy Modeling with Behavioral Signal Processing Framework." Current psychiatry reports vol. 18,5 (2016): 49. doi: https://doi.org/10.1007/s11920-016-0682-5
Xiao B, Imel ZE, Georgiou P, Atkins DC, Narayanan SS. Computational Analysis and Simulation of Empathic Behaviors: a Survey of Empathy Modeling with Behavioral Signal Processing Framework. Curr Psychiatry Rep. 2016 May;18(5):49. doi: 10.1007/s11920-016-0682-5. PMID: 27017830; PMCID: PMC5405768.
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Detecting paralinguistic events in audio stream using context in features and probabilistic decisions.

Computer speech & language

Gupta R, Audhkhasi K, Lee S, Narayanan S.
PMID: 28713197
Comput Speech Lang. 2016 Mar;36:72-92. doi: 10.1016/j.csl.2015.08.003. Epub 2015 Sep 11.

Non-verbal communication involves encoding, transmission and decoding of non-lexical cues and is realized using vocal (e.g. prosody) or visual (e.g. gaze, body language) channels during conversation. These cues perform the function of maintaining conversational flow, expressing emotions, and marking...

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Gupta R, Audhkhasi K, Lee S, et al. Detecting paralinguistic events in audio stream using context in features and probabilistic decisions. Comput Speech Lang. 2015;36:72-92doi: 10.1016/j.csl.2015.08.003.
Gupta, R., Audhkhasi, K., Lee, S., & Narayanan, S. (2016). Detecting paralinguistic events in audio stream using context in features and probabilistic decisions. Computer speech & language, 3672-92. https://doi.org/10.1016/j.csl.2015.08.003
Gupta, Rahul, et al. "Detecting paralinguistic events in audio stream using context in features and probabilistic decisions." Computer speech & language vol. 36 (2016): 72-92. doi: https://doi.org/10.1016/j.csl.2015.08.003
Gupta R, Audhkhasi K, Lee S, Narayanan S. Detecting paralinguistic events in audio stream using context in features and probabilistic decisions. Comput Speech Lang. 2016 Mar;36:72-92. doi: 10.1016/j.csl.2015.08.003. Epub 2015 Sep 11. PMID: 28713197; PMCID: PMC5507373.
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Can a computer detect interpersonal skills? Using machine learning to scale up the Facilitative Interpersonal Skills task.

Psychotherapy research : journal of the Society for Psychotherapy Research

Goldberg SB, Tanana M, Imel ZE, Atkins DC, Hill CE, Anderson T.
PMID: 32172682
Psychother Res. 2021 Mar;31(3):281-288. doi: 10.1080/10503307.2020.1741047. Epub 2020 Mar 16.

No abstract available.

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Goldberg SB, Tanana M, Imel ZE, et al. Can a computer detect interpersonal skills? Using machine learning to scale up the Facilitative Interpersonal Skills task. Psychother Res. 2020;31(3):281-288doi: 10.1080/10503307.2020.1741047.
Goldberg, S. B., Tanana, M., Imel, Z. E., Atkins, D. C., Hill, C. E., & Anderson, T. (2021). Can a computer detect interpersonal skills? Using machine learning to scale up the Facilitative Interpersonal Skills task. Psychotherapy research : journal of the Society for Psychotherapy Research, 31(3), 281-288. https://doi.org/10.1080/10503307.2020.1741047
Goldberg, Simon B, et al. "Can a computer detect interpersonal skills? Using machine learning to scale up the Facilitative Interpersonal Skills task." Psychotherapy research : journal of the Society for Psychotherapy Research vol. 31,3 (2021): 281-288. doi: https://doi.org/10.1080/10503307.2020.1741047
Goldberg SB, Tanana M, Imel ZE, Atkins DC, Hill CE, Anderson T. Can a computer detect interpersonal skills? Using machine learning to scale up the Facilitative Interpersonal Skills task. Psychother Res. 2021 Mar;31(3):281-288. doi: 10.1080/10503307.2020.1741047. Epub 2020 Mar 16. PMID: 32172682; PMCID: PMC7492408.
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Using Prosodic and Lexical Information for Learning Utterance-level Behaviors in Psychotherapy.

Interspeech

Singla K, Chen Z, Flemotomos N, Gibson J, Can D, Atkins DC, Narayanan S.
PMID: 34307639
Interspeech. 2018 Sep;2018:3413-3417. doi: 10.21437/interspeech.2018-2551.

In this paper, we present an approach for predicting utterance level behaviors in psychotherapy sessions using both speech and lexical features. We train long short term memory (LSTM) networks with an attention mechanism using words, both manually and automatically...

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Singla K, Chen Z, Flemotomos N, et al. Using Prosodic and Lexical Information for Learning Utterance-level Behaviors in Psychotherapy. Interspeech. 2018;2018:3413-3417doi: 10.21437/interspeech.2018-2551.
Singla, K., Chen, Z., Flemotomos, N., Gibson, J., Can, D., Atkins, D. C., & Narayanan, S. (2018). Using Prosodic and Lexical Information for Learning Utterance-level Behaviors in Psychotherapy. Interspeech, 20183413-3417. https://doi.org/10.21437/interspeech.2018-2551
Singla, Karan, et al. "Using Prosodic and Lexical Information for Learning Utterance-level Behaviors in Psychotherapy." Interspeech vol. 2018 (2018): 3413-3417. doi: https://doi.org/10.21437/interspeech.2018-2551
Singla K, Chen Z, Flemotomos N, Gibson J, Can D, Atkins DC, Narayanan S. Using Prosodic and Lexical Information for Learning Utterance-level Behaviors in Psychotherapy. Interspeech. 2018 Sep;2018:3413-3417. doi: 10.21437/interspeech.2018-2551. PMID: 34307639; PMCID: PMC8297805.
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Should we trust our judgments about the proficiency of Motivational Interviewing counselors? A glimpse at the impact of low inter-rater reliability.

Motivational interviewing : training, research, implementation, practice

Dunn C, Darnell D, Yi SK, Steyvers M, Bumgardner K, Lord SP, Imel Z, Atkins DC.
PMID: 27595051
Motiv Interviewing. 2014;1(3):38-41. doi: 10.5195/mitrip.2014.43.

Standardized rating systems are often used to evaluate the proficiency of Motivational Interviewing (MI) counselors. The published inter-rater reliability (degree of coder agreement) in many studies using these instruments has varied a great deal; some studies report MI proficiency...

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Dunn C, Darnell D, Yi SK, et al. Should we trust our judgments about the proficiency of Motivational Interviewing counselors? A glimpse at the impact of low inter-rater reliability. Motiv Interviewing. 2014;1(3):38-41doi: 10.5195/mitrip.2014.43.
Dunn, C., Darnell, D., Yi, S. K., Steyvers, M., Bumgardner, K., Lord, S. P., Imel, Z., & Atkins, D. C. (2014). Should we trust our judgments about the proficiency of Motivational Interviewing counselors? A glimpse at the impact of low inter-rater reliability. Motivational interviewing : training, research, implementation, practice, 1(3), 38-41. https://doi.org/10.5195/mitrip.2014.43
Dunn, Chris, et al. "Should we trust our judgments about the proficiency of Motivational Interviewing counselors? A glimpse at the impact of low inter-rater reliability." Motivational interviewing : training, research, implementation, practice vol. 1,3 (2014): 38-41. doi: https://doi.org/10.5195/mitrip.2014.43
Dunn C, Darnell D, Yi SK, Steyvers M, Bumgardner K, Lord SP, Imel Z, Atkins DC. Should we trust our judgments about the proficiency of Motivational Interviewing counselors? A glimpse at the impact of low inter-rater reliability. Motiv Interviewing. 2014;1(3):38-41. doi: 10.5195/mitrip.2014.43. PMID: 27595051; PMCID: PMC5008854.
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Rating motivational interviewing fidelity from thin slices.

Psychology of addictive behaviors : journal of the Society of Psychologists in Addictive Behaviors

Caperton DD, Atkins DC, Imel ZE.
PMID: 29723012
Psychol Addict Behav. 2018 Jun;32(4):434-441. doi: 10.1037/adb0000359. Epub 2018 May 03.

Monitoring fidelity to psychosocial treatments is critical to dissemination, process and outcome research, and internal validity in efficacy trials. However, the costs required to behavior code fidelity to treatments like motivational interviewing (MI) over many therapists and sessions quickly...

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Caperton DD, Atkins DC, Imel ZE. Rating motivational interviewing fidelity from thin slices. Psychol Addict Behav. 2018;32(4):434-441doi: 10.1037/adb0000359.
Caperton, D. D., Atkins, D. C., & Imel, Z. E. (2018). Rating motivational interviewing fidelity from thin slices. Psychology of addictive behaviors : journal of the Society of Psychologists in Addictive Behaviors, 32(4), 434-441. https://doi.org/10.1037/adb0000359
Caperton, Derek D, et al. "Rating motivational interviewing fidelity from thin slices." Psychology of addictive behaviors : journal of the Society of Psychologists in Addictive Behaviors vol. 32,4 (2018): 434-441. doi: https://doi.org/10.1037/adb0000359
Caperton DD, Atkins DC, Imel ZE. Rating motivational interviewing fidelity from thin slices. Psychol Addict Behav. 2018 Jun;32(4):434-441. doi: 10.1037/adb0000359. Epub 2018 May 03. PMID: 29723012; PMCID: PMC6013354.
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Transfer Learning from Adult to Children for Speech Recognition: Evaluation, Analysis and Recommendations.

Computer speech & language

Shivakumar PG, Georgiou P.
PMID: 32372847
Comput Speech Lang. 2020 Sep;63. doi: 10.1016/j.csl.2020.101077. Epub 2020 Feb 18.

Children speech recognition is challenging mainly due to the inherent high variability in children's physical and articulatory characteristics and expressions. This variability manifests in both acoustic constructs and linguistic usage due to the rapidly changing developmental stage in children's...

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Shivakumar PG, Georgiou P. Transfer Learning from Adult to Children for Speech Recognition: Evaluation, Analysis and Recommendations. Comput Speech Lang. 2020;63doi: 10.1016/j.csl.2020.101077.
Shivakumar, P. G., & Georgiou, P. (2020). Transfer Learning from Adult to Children for Speech Recognition: Evaluation, Analysis and Recommendations. Computer speech & language, 63. https://doi.org/10.1016/j.csl.2020.101077
Shivakumar, Prashanth Gurunath, and Georgiou, Panayiotis. "Transfer Learning from Adult to Children for Speech Recognition: Evaluation, Analysis and Recommendations." Computer speech & language vol. 63 (2020). doi: https://doi.org/10.1016/j.csl.2020.101077
Shivakumar PG, Georgiou P. Transfer Learning from Adult to Children for Speech Recognition: Evaluation, Analysis and Recommendations. Comput Speech Lang. 2020 Sep;63. doi: 10.1016/j.csl.2020.101077. Epub 2020 Feb 18. PMID: 32372847; PMCID: PMC7199459.
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A technology prototype system for rating therapist empathy from audio recordings in addiction counseling.

PeerJ. Computer science

Xiao B, Huang C, Imel ZE, Atkins DC, Georgiou P, Narayanan SS.
PMID: 28286867
PeerJ Comput Sci. 2016 Apr;2. doi: 10.7717/peerj-cs.59. Epub 2016 Apr 20.

Scaling up psychotherapy services such as for addiction counseling is a critical societal need. One challenge is ensuring quality of therapy, due to the heavy cost of manual observational assessment. This work proposes a speech technology-based system to automate...

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Xiao B, Huang C, Imel ZE, et al. A technology prototype system for rating therapist empathy from audio recordings in addiction counseling. PeerJ Comput Sci. 2016;2doi: 10.7717/peerj-cs.59.
Xiao, B., Huang, C., Imel, Z. E., Atkins, D. C., Georgiou, P., & Narayanan, S. S. (2016). A technology prototype system for rating therapist empathy from audio recordings in addiction counseling. PeerJ. Computer science, 2. https://doi.org/10.7717/peerj-cs.59
Xiao, Bo, et al. "A technology prototype system for rating therapist empathy from audio recordings in addiction counseling." PeerJ. Computer science vol. 2 (2016). doi: https://doi.org/10.7717/peerj-cs.59
Xiao B, Huang C, Imel ZE, Atkins DC, Georgiou P, Narayanan SS. A technology prototype system for rating therapist empathy from audio recordings in addiction counseling. PeerJ Comput Sci. 2016 Apr;2. doi: 10.7717/peerj-cs.59. Epub 2016 Apr 20. PMID: 28286867; PMCID: PMC5344199.
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Behavioral Signal Processing: Deriving Human Behavioral Informatics From Speech and Language: Computational techniques are presented to analyze and model expressed and perceived human behavior-variedly characterized as typical, atypical, distressed, and disordered-from speech and language cues and their applications in health, commerce, education, and beyond.

Proceedings of the IEEE. Institute of Electrical and Electronics Engineers

Narayanan S, Georgiou PG.
PMID: 24039277
Proc IEEE Inst Electr Electron Eng. 2013 Feb 07;101(5):1203-1233. doi: 10.1109/JPROC.2012.2236291.

The expression and experience of human behavior are complex and multimodal and characterized by individual and contextual heterogeneity and variability. Speech and spoken language communication cues offer an important means for measuring and modeling human behavior. Observational research and...

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Narayanan S, Georgiou PG. Behavioral Signal Processing: Deriving Human Behavioral Informatics From Speech and Language: Computational techniques are presented to analyze and model expressed and perceived human behavior-variedly characterized as typical, atypical, distressed, and disordered-from speech and language cues and their applications in health, commerce, education, and beyond. Proc IEEE Inst Electr Electron Eng. 2013;101(5):1203-1233doi: 10.1109/JPROC.2012.2236291.
Narayanan, S., & Georgiou, P. G. (2013). Behavioral Signal Processing: Deriving Human Behavioral Informatics From Speech and Language: Computational techniques are presented to analyze and model expressed and perceived human behavior-variedly characterized as typical, atypical, distressed, and disordered-from speech and language cues and their applications in health, commerce, education, and beyond. Proceedings of the IEEE. Institute of Electrical and Electronics Engineers, 101(5), 1203-1233. https://doi.org/10.1109/JPROC.2012.2236291
Narayanan, Shrikanth, and Georgiou, Panayiotis G. "Behavioral Signal Processing: Deriving Human Behavioral Informatics From Speech and Language: Computational techniques are presented to analyze and model expressed and perceived human behavior-variedly characterized as typical, atypical, distressed, and disordered-from speech and language cues and their applications in health, commerce, education, and beyond." Proceedings of the IEEE. Institute of Electrical and Electronics Engineers vol. 101,5 (2013): 1203-1233. doi: https://doi.org/10.1109/JPROC.2012.2236291
Narayanan S, Georgiou PG. Behavioral Signal Processing: Deriving Human Behavioral Informatics From Speech and Language: Computational techniques are presented to analyze and model expressed and perceived human behavior-variedly characterized as typical, atypical, distressed, and disordered-from speech and language cues and their applications in health, commerce, education, and beyond. Proc IEEE Inst Electr Electron Eng. 2013 Feb 07;101(5):1203-1233. doi: 10.1109/JPROC.2012.2236291. PMID: 24039277; PMCID: PMC3769794.
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Head Motion Modeling for Human Behavior Analysis in Dyadic Interaction.

IEEE transactions on multimedia

Xiao B, Georgiou P, Baucom B, Narayanan SS.
PMID: 26557047
IEEE Trans Multimedia. 2015 Jul 13;17(7):1107-1119. doi: 10.1109/TMM.2015.2432671. Epub 2015 May 13.

This paper presents a computational study of head motion in human interaction, notably of its role in conveying interlocutors' behavioral characteristics. Head motion is physically complex and carries rich information; current modeling approaches based on visual signals, however, are...

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Xiao B, Georgiou P, Baucom B, et al. Head Motion Modeling for Human Behavior Analysis in Dyadic Interaction. IEEE Trans Multimedia. 2015;17(7):1107-1119doi: 10.1109/TMM.2015.2432671.
Xiao, B., Georgiou, P., Baucom, B., & Narayanan, S. S. (2015). Head Motion Modeling for Human Behavior Analysis in Dyadic Interaction. IEEE transactions on multimedia, 17(7), 1107-1119. https://doi.org/10.1109/TMM.2015.2432671
Xiao, Bo, et al. "Head Motion Modeling for Human Behavior Analysis in Dyadic Interaction." IEEE transactions on multimedia vol. 17,7 (2015): 1107-1119. doi: https://doi.org/10.1109/TMM.2015.2432671
Xiao B, Georgiou P, Baucom B, Narayanan SS. Head Motion Modeling for Human Behavior Analysis in Dyadic Interaction. IEEE Trans Multimedia. 2015 Jul 13;17(7):1107-1119. doi: 10.1109/TMM.2015.2432671. Epub 2015 May 13. PMID: 26557047; PMCID: PMC4636041.
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Barista: A Framework for Concurrent Speech Processing by USC-SAIL.

Proceedings of the ... IEEE International Conference on Acoustics, Speech, and Signal Processing. ICASSP (Conference)

Can D, Gibson J, Vaz C, Georgiou PG, Narayanan SS.
PMID: 27610047
Proc IEEE Int Conf Acoust Speech Signal Process. 2014 May;2014:3306-3310. doi: 10.1109/ICASSP.2014.6854212.

We present Barista, an open-source framework for concurrent speech processing based on the Kaldi speech recognition toolkit and the libcppa actor library. With Barista, we aim to provide an easy-to-use, extensible framework for constructing highly customizable concurrent (and/or distributed)...

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Can D, Gibson J, Vaz C, et al. Barista: A Framework for Concurrent Speech Processing by USC-SAIL. Proc IEEE Int Conf Acoust Speech Signal Process. 2014;2014:3306-3310doi: 10.1109/ICASSP.2014.6854212.
Can, D., Gibson, J., Vaz, C., Georgiou, P. G., & Narayanan, S. S. (2014). Barista: A Framework for Concurrent Speech Processing by USC-SAIL. Proceedings of the ... IEEE International Conference on Acoustics, Speech, and Signal Processing. ICASSP (Conference), 20143306-3310. https://doi.org/10.1109/ICASSP.2014.6854212
Can, Doğan, et al. "Barista: A Framework for Concurrent Speech Processing by USC-SAIL." Proceedings of the ... IEEE International Conference on Acoustics, Speech, and Signal Processing. ICASSP (Conference) vol. 2014 (2014): 3306-3310. doi: https://doi.org/10.1109/ICASSP.2014.6854212
Can D, Gibson J, Vaz C, Georgiou PG, Narayanan SS. Barista: A Framework for Concurrent Speech Processing by USC-SAIL. Proc IEEE Int Conf Acoust Speech Signal Process. 2014 May;2014:3306-3310. doi: 10.1109/ICASSP.2014.6854212. PMID: 27610047; PMCID: PMC5012110.
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Showing 1 to 12 of 17 entries