34 citations · 37 across the 13 of their papers we have counts for
17 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…
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…
Time-Resolved EEG Decoding of Semantic Processing Reveals Altered Neural Dynamics in Depression and Suicidality
Woojae Jeong, Aditya Kommineni, Kleanthis Avramidis +15
Depression and suicidality affect cognitive and emotional processes, yet objective, task-evoked neural readouts of mental health remain limited. We investigated the spatiotemporal…
Neural Responses to Affective Sentences Reveal Signatures of Depression
Aditya Kommineni, Woojae Jeong, Kleanthis Avramidis +13
Major Depressive Disorder (MDD) is a highly prevalent mental health condition, and a deeper understanding of its neurocognitive foundations is essential for identifying how core fu…
Deep Learning Characterizes Depression and Suicidal Ideation from Eye Movements
Kleanthis Avramidis, Woojae Jeong, Aditya Kommineni +14
Identifying physiological and behavioral markers for mental health conditions is a longstanding challenge in psychiatry. Depression and suicidal ideation, in particular, lack objec…
Enhancing Listened Speech Decoding from EEG via Parallel Phoneme Sequence Prediction
Jihwan Lee, Tiantian Feng, Aditya Kommineni +2
Brain-computer interfaces (BCI) offer numerous human-centered application possibilities, particularly affecting people with neurological disorders. Text or speech decoding from bra…
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…
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…
Toward Fully-End-to-End Listened Speech Decoding from EEG Signals
Jihwan Lee, Aditya Kommineni, Tiantian Feng +4
Speech decoding from EEG signals is a challenging task, where brain activity is modeled to estimate salient characteristics of acoustic stimuli. We propose FESDE, a novel framework…
Knowledge-guided EEG Representation Learning
Aditya Kommineni, Kleanthis Avramidis, Richard Leahy +1
Self-supervised learning has produced impressive results in multimedia domains of audio, vision and speech. This paradigm is equally, if not more, relevant for the domain of biosig…
A Review of Speech-centric Trustworthy Machine Learning: Privacy, Safety, and Fairness
Tiantian Feng, Rajat Hebbar, Nicholas Mehlman +3
Speech-centric machine learning systems have revolutionized many leading domains ranging from transportation and healthcare to education and defense, profoundly changing how people…