collaborators

10 papers

eess.AS2026

From Signals to Patterns: Non-Invasive Tuberculosis Detection from Cough Audio using Bandit Weighted Hyperbolic Prototypes

Mohd Mujtaba Akhtar, Girish, Sanjam Wadhwa +2

In this study, we focus on cough-based tuberculosis screening (CBTS) and hypothesize that fusing speech/audio foundation representations with spectral descriptors will yield strong…

eess.AS2026

Synergizing Zero-Shot Cross-Lingual Alzheimer Detection with Language-Invariant Multimodal Bi-Geometric Adversarial Learning

Girish, Mohd Mujtaba Akhtar, Farhan Sheth +4

In this work, we study zero-shot cross-lingual speech-based Alzheimer's disease detection (SADD). We hypothesize that learning language-invariant multimodal representations by fusi…

eess.AS2026

Prosody as Supervision: Bridging the Non-Verbal--Verbal for Multilingual Speech Emotion Recognition

Girish, Mohd Mujtaba Akhtar, Muskaan Singh

In this work, we introduce a paralinguistic supervision paradigm for low-resource multilingual speech emotion recognition (LRM-SER) that leverages non-verbal vocalizations to explo…

eess.AS2026

HCFD: A Benchmark for Audio Deepfake Detection in Healthcare

Mohd Mujtaba Akhtar, Girish, Muskaan Singh

In this study, we present Healthcare Codec-Fake Detection (HCFD), a new task for detecting codec-fakes under pathological speech conditions. We intentionally focus on codec based s…

eess.AS2026

Bridging Attribution and Open-Set Detection using Graph-Augmented Instance Learning in Synthetic Speech

Mohd Mujtaba Akhtar, Girish, Farhan Sheth +1

We propose a unified framework for not only attributing synthetic speech to its source but also for detecting speech generated by synthesizers that were not encountered during trai…

eess.AS2026

DIVINE: Coordinating Multimodal Disentangled Representations for Oro-Facial Neurological Disorder Assessment

Mohd Mujtaba Akhtar, Girish, Muskaan Singh

In this study, we present a multimodal framework for predicting neuro-facial disorders by capturing both vocal and facial cues. We hypothesize that explicitly disentangling shared…