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Curved Worlds, Clear Boundaries: Generalizing Speech Deepfake Detection using Hyperbolic and Spherical Geometry Spaces
Farhan Sheth, Girish, Mohd Mujtaba Akhtar +1
In this work, we address the challenge of generalizable audio deepfake detection (ADD) across diverse speech synthesis paradigms-including conventional text-to-speech (TTS) systems…
Towards Attribution of Generators and Emotional Manipulation in Cross-Lingual Synthetic Speech using Geometric Learning
Girish, Mohd Mujtaba Akhtar, Farhan Sheth +1
In this work, we address the problem of finegrained traceback of emotional and manipulation characteristics from synthetically manipulated speech. We hypothesize that combining sem…
Investigating Polyglot Speech Foundation Models for Learning Collective Emotion from Crowds
Orchid Chetia Phukan, Girish, Mohd Mujtaba Akhtar +6
This paper investigates the polyglot (multilingual) speech foundation models (SFMs) for Crowd Emotion Recognition (CER). We hypothesize that polyglot SFMs, pre-trained on diverse l…
Are Multimodal Foundation Models All That Is Needed for Emofake Detection?
Mohd Mujtaba Akhtar, Girish, Orchid Chetia Phukan +5
In this work, we investigate multimodal foundation models (MFMs) for EmoFake detection (EFD) and hypothesize that they will outperform audio foundation models (AFMs). MFMs due to t…
Rethinking Cross-Corpus Speech Emotion Recognition Benchmarking: Are Paralinguistic Pre-Trained Representations Sufficient?
Orchid Chetia Phukan, Mohd Mujtaba Akhtar, Girish +4
Recent benchmarks evaluating pre-trained models (PTMs) for cross-corpus speech emotion recognition (SER) have overlooked PTM pre-trained for paralinguistic speech processing (PSP),…
Enhancing In-Domain and Out-Domain EmoFake Detection via Cooperative Multilingual Speech Foundation Models
Orchid Chetia Phukan, Mohd Mujtaba Akhtar, Girish +1
In this work, we address EmoFake Detection (EFD). We hypothesize that multilingual speech foundation models (SFMs) will be particularly effective for EFD due to their pre-training…
Towards Neural Audio Codec Source Parsing
Orchid Chetia Phukan, Girish, Mohd Mujtaba Akhtar +2
A new class of audio deepfakes-codecfakes (CFs)-has recently caught attention, synthesized by Audio Language Models that leverage neural audio codecs (NACs) in the backend. In resp…
HYFuse: Aligning Heterogeneous Speech Pre-Trained Representations in Hyperbolic Space for Speech Emotion Recognition
Orchid Chetia Phukan, Girish, Mohd Mujtaba Akhtar +4
Compression-based representations (CBRs) from neural audio codecs such as EnCodec capture intricate acoustic features like pitch and timbre, while representation-learning-based rep…
SNIFR : Boosting Fine-Grained Child Harmful Content Detection Through Audio-Visual Alignment with Cascaded Cross-Transformer
Orchid Chetia Phukan, Mohd Mujtaba Akhtar, Girish +8
As video-sharing platforms have grown over the past decade, child viewership has surged, increasing the need for precise detection of harmful content like violence or explicit scen…
Towards Source Attribution of Singing Voice Deepfake with Multimodal Foundation Models
Orchid Chetia Phukan, Girish, Mohd Mujtaba Akhtar +5
In this work, we introduce the task of singing voice deepfake source attribution (SVDSA). We hypothesize that multimodal foundation models (MMFMs) such as ImageBind, LanguageBind w…
Are Mamba-based Audio Foundation Models the Best Fit for Non-Verbal Emotion Recognition?
Mohd Mujtaba Akhtar, Orchid Chetia Phukan, Girish +5
In this work, we focus on non-verbal vocal sounds emotion recognition (NVER). We investigate mamba-based audio foundation models (MAFMs) for the first time for NVER and hypothesize…
Investigating the Reasonable Effectiveness of Speaker Pre-Trained Models and their Synergistic Power for SingMOS Prediction
Orchid Chetia Phukan, Girish, Mohd Mujtaba Akhtar +4
In this study, we focus on Singing Voice Mean Opinion Score (SingMOS) prediction. Previous research have shown the performance benefit with the use of state-of-the-art (SOTA) pre-t…
Towards Machine Unlearning for Paralinguistic Speech Processing
Orchid Chetia Phukan, Girish, Mohd Mujtaba Akhtar +6
In this work, we pioneer the study of Machine Unlearning (MU) for Paralinguistic Speech Processing (PSP). We focus on two key PSP tasks: Speech Emotion Recognition (SER) and Depres…
Source Tracing of Synthetic Speech Systems Through Paralinguistic Pre-Trained Representations
Girish, Mohd Mujtaba Akhtar, Orchid Chetia Phukan +5
In this work, we focus on source tracing of synthetic speech generation systems (STSGS). Each source embeds distinctive paralinguistic features--such as pitch, tone, rhythm, and in…
Towards Fusion of Neural Audio Codec-based Representations with Spectral for Heart Murmur Classification via Bandit-based Cross-Attention Mechanism
Orchid Chetia Phukan, Girish, Mohd Mujtaba Akhtar +5
In this study, we focus on heart murmur classification (HMC) and hypothesize that combining neural audio codec representations (NACRs) such as EnCodec with spectral features (SFs),…
PARROT: Synergizing Mamba and Attention-based SSL Pre-Trained Models via Parallel Branch Hadamard Optimal Transport for Speech Emotion Recognition
Orchid Chetia Phukan, Mohd Mujtaba Akhtar, Girish +4
The emergence of Mamba as an alternative to attention-based architectures has led to the development of Mamba-based self-supervised learning (SSL) pre-trained models (PTMs) for spe…