22 citations · 31 across the 7 of their papers we have counts for
7 papers
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…
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…