9 papers
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…
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…
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…