2 citations · 6 across the 9 of their papers we have counts for
9 papers
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…
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…
Partial Federated Learning
Tiantian Feng, Anil Ramakrishna, Jimit Majmudar +6
Federated Learning (FL) is a popular algorithm to train machine learning models on user data constrained to edge devices (for example, mobile phones) due to privacy concerns. Typic…
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…
Foundation Model Assisted Automatic Speech Emotion Recognition: Transcribing, Annotating, and Augmenting
Tiantian Feng, Shrikanth Narayanan
Significant advances are being made in speech emotion recognition (SER) using deep learning models. Nonetheless, training SER systems remains challenging, requiring both time and c…