7 citations · 18 across the 41 of their papers we have counts for
14 papers · 1 filter
Can Generic LLMs Help Analyze Child-adult Interactions Involving Children with Autism in Clinical Observation?
Tiantian Feng, Anfeng Xu, Rimita Lahiri +5
Large Language Models (LLMs) have shown significant potential in understanding human communication and interaction. However, their performance in the domain of child-inclusive inte…
Speech2rtMRI: Speech-Guided Diffusion Model for Real-time MRI Video of the Vocal Tract during Speech
Hong Nguyen, Sean Foley, Kevin Huang +3
Understanding speech production both visually and kinematically can inform second language learning system designs, as well as the creation of speaking characters in video games an…
Towards Child-Inclusive Clinical Video Understanding for Autism Spectrum Disorder
Aditya Kommineni, Digbalay Bose, Tiantian Feng +6
Clinical videos in the context of Autism Spectrum Disorder are often long-form interactions between children and caregivers/clinical professionals, encompassing complex verbal and…
Data Efficient Child-Adult Speaker Diarization with Simulated Conversations
Anfeng Xu, Tiantian Feng, Helen Tager-Flusberg +2
Automating child speech analysis is crucial for applications such as neurocognitive assessments. Speaker diarization, which identifies ``who spoke when'', is an essential component…
Examining Test-Time Adaptation for Personalized Child Speech Recognition
Zhonghao Shi, Xuan Shi, Anfeng Xu +4
Automatic speech recognition (ASR) models often experience performance degradation due to data domain shifts introduced at test time, a challenge that is further amplified for chil…
Egocentric Speaker Classification in Child-Adult Dyadic Interactions: From Sensing to Computational Modeling
Tiantian Feng, Anfeng Xu, Xuan Shi +2
Autism spectrum disorder (ASD) is a neurodevelopmental condition characterized by challenges in social communication, repetitive behavior, and sensory processing. One important res…
ModalityMirror: Improving Audio Classification in Modality Heterogeneity Federated Learning with Multimodal Distillation
Tiantian Feng, Tuo Zhang, Salman Avestimehr +1
Multimodal Federated Learning frequently encounters challenges of client modality heterogeneity, leading to undesired performances for secondary modality in multimodal learning. It…
Creating a Lens of Chinese Culture: A Multimodal Dataset for Chinese Pun Rebus Art Understanding
Tuo Zhang, Tiantian Feng, Yibin Ni +7
Large vision-language models (VLMs) have demonstrated remarkable abilities in understanding everyday content. However, their performance in the domain of art, particularly cultural…
Toward Fully-End-to-End Listened Speech Decoding from EEG Signals
Jihwan Lee, Aditya Kommineni, Tiantian Feng +4
Speech decoding from EEG signals is a challenging task, where brain activity is modeled to estimate salient characteristics of acoustic stimuli. We propose FESDE, a novel framework…
Exploring Speech Foundation Models for Speaker Diarization in Child-Adult Dyadic Interactions
Anfeng Xu, Kevin Huang, Tiantian Feng +3
Speech foundation models, trained on vast datasets, have opened unique opportunities in addressing challenging low-resource speech understanding, such as child speech. In this work…
Can Synthetic Audio From Generative Foundation Models Assist Audio Recognition and Speech Modeling?
Tiantian Feng, Dimitrios Dimitriadis, Shrikanth Narayanan
Recent advances in foundation models have enabled audio-generative models that produce high-fidelity sounds associated with music, events, and human actions. Despite the success ac…
TI-ASU: Toward Robust Automatic Speech Understanding through Text-to-speech Imputation Against Missing Speech Modality
Tiantian Feng, Xuan Shi, Rahul Gupta +1
Automatic Speech Understanding (ASU) aims at human-like speech interpretation, providing nuanced intent, emotion, sentiment, and content understanding from speech and language (tex…
Can Text-to-image Model Assist Multi-modal Learning for Visual Recognition with Visual Modality Missing?
Tiantian Feng, Daniel Yang, Digbalay Bose +1
Multi-modal learning has emerged as an increasingly promising avenue in vision recognition, driving innovations across diverse domains ranging from media and education to healthcar…
Understanding Stress, Burnout, and Behavioral Patterns in Medical Residents Using Large-scale Longitudinal Wearable Recordings
Tiantian Feng, Shrikanth Narayanan
Medical residency training is often associated with physically intense and emotionally demanding tasks, requiring them to engage in extended working hours providing complex clinica…