activity
20202026
most citedTowards Child-Inclusive Clinical Video Understanding for Autism Spectrum Disorder

1 citations · 7 across the 41 of their papers we have counts for

collaborators

61 papers

eess.AS2026

Speaker Role and Language Diarization for Analyzing Multilingual Interviews for Language Proficiency of Older Adults

Anfeng Xu, Tiantian Feng, Kevin Huang +8

Automatic language proficiency assessment in the context of multilingual interview-based settings remains underexplored. In this work, we develop Whisper-based speaker-role and lan…

eess.AS2026

RRP-Voice: A Longitudinal Dataset and Benchmark for Recurrent Respiratory Papillomatosis Detection

Wenze Ren, Ke-Han Lu, Kai-Wei Chang +8

Deep learning has advanced pathological voice detection rapidly, yet rare laryngeal diseases remain underexplored due to data scarcity. Recurrent Respiratory Papillomatosis (RRP) e…

cs.SD2026

ChildVox: A Speech, Audio, and Large Audio-Language Model Benchmark in Understanding and Characterizing Sound across Childhood

Tiantian Feng, Anfeng Xu, Xuan Shi +10

We present ChildVox, a novel benchmark for characterizing the diverse acoustic signals through which children communicate. Specifically, ChildVox follows the full developmental tra…

cs.LG2026

A Multi-dimensional Framework for Evaluating Generalization in EEG Foundation Models

Aditya Kommineni, Emily Zhou, Kleanthis Avramidis +2

Evaluating foundation models under appropriate adaptation settings is essential for understanding the quality and transferability of the learned representations. Recent EEG foundat…

cs.LG2026

Aperiodic and Low-Frequency Spectral Bias in Reconstruction based EEG Foundation Models

Aditya Kommineni, Emily Zhou, Kleanthis Avramidis +7

EEG foundation models, pre-trained on large-scale unlabelled EEG data, have emerged as a promising direction towards learning generalizable EEG representations. Despite showing pos…

eess.AS2026

Exploring Speech Foundation Models for Speaker Diarization Across Lifespan

Anfeng Xu, Tiantian Feng, Shrikanth Narayanan

Speech foundation models have shown strong transferability across a wide range of speech applications. However, their robustness to age-related domain shift in speaker diarization…