22 citations · 40 across the 12 of their papers we have counts for
12 papers
Assessing Algorithmic Bias in Language-Based Depression Detection: A Comparison of DNN and LLM Approaches
Obed Junias, Prajakta Kini, Theodora Chaspari
This paper investigates algorithmic bias in language-based models for automated depression detection, focusing on socio-demographic disparities related to gender and race/ethnicity…
Assessing the feasibility of Large Language Models for detecting micro-behaviors in team interactions during space missions
Ankush Raut, Projna Paromita, Sydney Begerowski +2
We explore the feasibility of large language models (LLMs) in detecting subtle expressions of micro-behaviors in team conversations using transcripts collected during simulated spa…
Robust and Explainable Depression Identification from Speech Using Vowel-Based Ensemble Learning Approaches
Kexin Feng, Theodora Chaspari
This study investigates explainable machine learning algorithms for identifying depression from speech. Grounded in evidence from speech production that depression affects motor co…
A Pilot Study on Clinician-AI Collaboration in Diagnosing Depression from Speech
Kexin Feng, Theodora Chaspari
This study investigates clinicians' perceptions and attitudes toward an assistive artificial intelligence (AI) system that employs a speech-based explainable ML algorithm for detec…
A knowledge-driven vowel-based approach of depression classification from speech using data augmentation
Kexin Feng, Theodora Chaspari
We propose a novel explainable machine learning (ML) model that identifies depression from speech, by modeling the temporal dependencies across utterances and utilizing the spectro…
A few-shot learning approach with domain adaptation for personalized real-life stress detection in close relationships
Kexin Feng, Jacqueline B. Duong, Kayla E. Carta +4
We design a metric learning approach that aims to address computational challenges that yield from modeling human outcomes from ambulatory real-life data. The proposed metric learn…
Toward Knowledge-Driven Speech-Based Models of Depression: Leveraging Spectrotemporal Variations in Speech Vowels
Kexin Feng, Theodora Chaspari
Psychomotor retardation associated with depression has been linked with tangible differences in vowel production. This paper investigates a knowledge-driven machine learning (ML) m…
Predicting the meal macronutrient composition from continuous glucose monitors
Zepeng Huo, Bobak J. Mortazavi, Theodora Chaspari +3
Sustained high levels of blood glucose in type 2 diabetes (T2DM) can have disastrous long-term health consequences. An essential component of clinical interventions for T2DM is mon…
Few-shot Learning in Emotion Recognition of Spontaneous Speech Using a Siamese Neural Network with Adaptive Sample Pair Formation
Kexin Feng, Theodora Chaspari
Speech-based machine learning (ML) has been heralded as a promising solution for tracking prosodic and spectrotemporal patterns in real-life that are indicative of emotional change…
Exploring Speech Cues in Web-mined COVID-19 Conversational Vlogs
Kexin Feng, Preeti Zanwar, Amir H. Behzadan +1
The COVID-19 pandemic caused by the novel SARS-Coronavirus-2 (n-SARS-CoV-2) has impacted people's lives in unprecedented ways. During the time of the pandemic, social vloggers have…
A Siamese Neural Network with Modified Distance Loss For Transfer Learning in Speech Emotion Recognition
Kexin Feng, Theodora Chaspari
Automatic emotion recognition plays a significant role in the process of human computer interaction and the design of Internet of Things (IOT) technologies. Yet, a common problem i…
An adversarial learning framework for preserving users' anonymity in face-based emotion recognition
Vansh Narula, Zhangyang, Wang +1
Image and video-capturing technologies have permeated our every-day life. Such technologies can continuously monitor individuals' expressions in real-life settings, affording us ne…