34 citations · 37 across the 13 of their papers we have counts for
8 papers · 1 filter
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…
Best Practices in EEG Analysis: Preprocessing, Modeling, and Machine Learning
Parsa Razmara, Woojae Jeong, Aditya Kommineni +3
Electroencephalography (EEG) analysis requires careful choices in preprocessing, statistical modeling, and machine learning because EEG signals are highly susceptible to artifacts,…
Towards Interpretable Framework for Neural Audio Codecs via Sparse Autoencoders: Exploration toward Age, Gender, and Accent Steering
Shih-Heng Wang, Tiantian Feng, Aditya Kommineni +4
Neural audio codecs (NACs) are widely used in speech generation and audio-language modeling, yet how they encode speaker-trait information remains poorly understood. Prior work app…
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…
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…
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…
Towards Interpretable Framework for Neural Audio Codecs via Sparse Autoencoders: A Case Study on Accent Information
Shih-Heng Wang, Tiantian Feng, Aditya Kommineni +4
Neural Audio Codecs (NACs) are widely adopted in modern speech systems, yet how they encode linguistic and paralinguistic information remains unclear. Improving the interpretabilit…
An Approach to Simultaneous Acquisition of Real-Time MRI Video, EEG, and Surface EMG for Articulatory, Brain, and Muscle Activity During Speech Production
Jihwan Lee, Parsa Razmara, Kevin Huang +16
Speech production is a complex process spanning neural planning, motor control, muscle activation, and articulatory kinematics. While the acoustic speech signal is the most accessi…