4 citations · 11 across the 17 of their papers we have counts for
13 papers · 1 filter
Speaker Verification Under Real Classroom Conditions for English Speech
Saba Tabatabaee, Jing Liu, Meghavarshini Krishnaswamy +1
Developing speaker verification (SV) models that are robust to classroom noise and effective across both children and adult speakers is critical for AI tools supporting educational…
Speech-Based Prioritization for Schizophrenia Intervention
Gowtham Premananth, Philip Resnik, Sonia Bansal +2
Millions of people suffer from mental health conditions, yet many remain undiagnosed or receive delayed care due to limited clinical resources and labor-intensive assessment method…
Quantifying Articulatory Coordination as a Biomarker for Schizophrenia
Gowtham Premananth, Carol Espy-Wilson
Advances in artificial intelligence (AI) and deep learning have improved diagnostic capabilities in healthcare, yet limited interpretability continues to hinder clinical adoption.…
Multimodal Biomarkers for Schizophrenia: Towards Individual Symptom Severity Estimation
Gowtham Premananth, Philip Resnik, Sonia Bansal +2
Studies on schizophrenia assessments using deep learning typically treat it as a classification task to detect the presence or absence of the disorder, oversimplifying the conditio…
Analyzing the Impact of Accent on English Speech: Acoustic and Articulatory Perspectives
Gowtham Premananth, Vinith Kugathasan, Carol Espy-Wilson
Advancements in AI-driven speech-based applications have transformed diverse industries ranging from healthcare to customer service. However, the increasing prevalence of non-nativ…
FT-Boosted SV: Towards Noise Robust Speaker Verification for English Speaking Classroom Environments
Saba Tabatabaee, Jing Liu, Carol Espy-Wilson
Creating Speaker Verification (SV) systems for classroom settings that are robust to classroom noises such as babble noise is crucial for the development of AI tools that assist ed…
From Weak Labels to Strong Results: Utilizing 5,000 Hours of Noisy Classroom Transcripts with Minimal Accurate Data
Ahmed Adel Attia, Dorottya Demszky, Jing Liu +1
Recent progress in speech recognition has relied on models trained on vast amounts of labeled data. However, classroom Automatic Speech Recognition (ASR) faces the real-world chall…
Speech-Based Estimation of Schizophrenia Severity Using Feature Fusion
Gowtham Premananth, Carol Espy-Wilson
Speech-based assessment of the schizophrenia spectrum has been widely researched over in the recent past. In this study, we develop a deep learning framework to estimate schizophre…
Self-supervised Multimodal Speech Representations for the Assessment of Schizophrenia Symptoms
Gowtham Premananth, Carol Espy-Wilson
Multimodal schizophrenia assessment systems have gained traction over the last few years. This work introduces a schizophrenia assessment system to discern between prominent sympto…
A Multimodal Framework for the Assessment of the Schizophrenia Spectrum
Gowtham Premananth, Yashish M. Siriwardena, Philip Resnik +3
This paper presents a novel multimodal framework to distinguish between different symptom classes of subjects in the schizophrenia spectrum and healthy controls using audio, video,…
Accent Conversion with Articulatory Representations
Yashish M. Siriwardena, Nathan Swedlow, Audrey Howard +4
Conversion of non-native accented speech to native (American) English has a wide range of applications such as improving intelligibility of non-native speech. Previous work on this…
Improving Speech Inversion Through Self-Supervised Embeddings and Enhanced Tract Variables
Ahmed Adel Attia, Yashish M. Siriwardena, Carol Espy-Wilson
The performance of deep learning models depends significantly on their capacity to encode input features efficiently and decode them into meaningful outputs. Better input and outpu…
Kid-Whisper: Towards Bridging the Performance Gap in Automatic Speech Recognition for Children VS. Adults
Ahmed Adel Attia, Jing Liu, Wei Ai +2
Recent advancements in Automatic Speech Recognition (ASR) systems, exemplified by Whisper, have demonstrated the potential of these systems to approach human-level performance give…