1 citations · 3 across the 7 of their papers we have counts for
8 papers
Towards Automated Clinical Behavioral Coding with Large Language Models: A Case study Using BOSCC recordings of Children
Jordan Prescott, Aditya Kommineni, Tiantian Feng +8
Autism spectrum disorder (ASD) is a neurodevelopmental condition characterized by differences in social communication and by restricted interests and repetitive behaviors. Treatmen…
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…
End-to-End Joint ASR and Speaker Role Diarization with Child-Adult Interactions
Anfeng Xu, Tiantian Feng, Somer Bishop +2
Accurate transcription and speaker role diarization of child-adult spoken interactions are crucial for developmental and clinical research. However, manual annotation is time-consu…
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…
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…
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…
Evaluation of Speech Foundation Models for ASR on Child-Adult Conversations in Autism Diagnostic Sessions
Aditya Ashvin, Rimita Lahiri, Aditya Kommineni +4
Reliable transcription of child-adult conversations in clinical settings is crucial for diagnosing developmental disorders like Autism. Recent advances in deep learning and availab…