activity
20242026
most citedCan Generic LLMs Help Analyze Child-adult Interactions Involving Children with Autism in Clinical Observation?

1 citations · 1 across the 5 of their papers we have counts for

collaborators

7 papers

cs.SD2026

VoxCog: Towards End-to-End Multilingual Cognitive Impairment Classification through Dialectal Knowledge

Tiantian Feng, Anfeng Xu, Jinkook Lee +1

In this work, we present a novel perspective on cognitive impairment classification from speech by integrating speech foundation models that explicitly recognize speech dialects. O…

cs.SD2025

Voxlect: A Speech Foundation Model Benchmark for Modeling Dialects and Regional Languages Around the Globe

Tiantian Feng, Kevin Huang, Anfeng Xu +6

We present Voxlect, a novel benchmark for modeling dialects and regional languages worldwide using speech foundation models. Specifically, we report comprehensive benchmark evaluat…

eess.AS2025

Joint ASR and Speaker Role Tagging with Serialized Output Training

Anfeng Xu, Tiantian Feng, Shrikanth Narayanan

Automatic Speech Recognition systems have made significant progress with large-scale pre-trained models. However, most current systems focus solely on transcribing the speech witho…

cs.CL2025

Large Language Models based ASR Error Correction for Child Conversations

Anfeng Xu, Tiantian Feng, So Hyun Kim +3

Automatic Speech Recognition (ASR) has recently shown remarkable progress, but accurately transcribing children's speech remains a significant challenge. Recent developments in Lar…

cs.SD2025

Vox-Profile: A Speech Foundation Model Benchmark for Characterizing Diverse Speaker and Speech Traits

Tiantian Feng, Jihwan Lee, Anfeng Xu +9

We introduce Vox-Profile, a comprehensive benchmark to characterize rich speaker and speech traits using speech foundation models. Unlike existing works that focus on a single dime…

eess.AS2025

Who Said What WSW 2.0? Enhanced Automated Analysis of Preschool Classroom Speech

Anchen Sun, Tiantian Feng, Gabriela Gutierrez +6

This paper introduces an automated framework WSW2.0 for analyzing vocal interactions in preschool classrooms, enhancing both accuracy and scalability through the integration of wav…