1 citations · 5 across the 28 of their papers we have counts for
17 papers · 1 filter
Investigating Prosodic Signatures via Speech Pre-Trained Models for Audio Deepfake Source Attribution
Orchid Chetia Phukan, Drishti Singh, Swarup Ranjan Behera +2
In this work, we investigate various state-of-the-art (SOTA) speech pre-trained models (PTMs) for their capability to capture prosodic signatures of the generative sources for audi…
Multi-View Multi-Task Modeling with Speech Foundation Models for Speech Forensic Tasks
Orchid Chetia Phukan, Devyani Koshal, Swarup Ranjan Behera +2
Speech forensic tasks (SFTs), such as automatic speaker recognition (ASR), speech emotion recognition (SER), gender recognition (GR), and age estimation (AE), find use in different…
Beyond Speech and More: Investigating the Emergent Ability of Speech Foundation Models for Classifying Physiological Time-Series Signals
Orchid Chetia Phukan, Swarup Ranjan Behera, Girish +3
Despite being trained exclusively on speech data, speech foundation models (SFMs) like Whisper have shown impressive performance in non-speech tasks such as audio classification. T…
SeQuiFi: Mitigating Catastrophic Forgetting in Speech Emotion Recognition with Sequential Class-Finetuning
Sarthak Jain, Orchid Chetia Phukan, Swarup Ranjan Behera +2
In this work, we introduce SeQuiFi, a novel approach for mitigating catastrophic forgetting (CF) in speech emotion recognition (SER). SeQuiFi adopts a sequential class-finetuning s…
Representation Loss Minimization with Randomized Selection Strategy for Efficient Environmental Fake Audio Detection
Orchid Chetia Phukan, Girish, Mohd Mujtaba Akhtar +5
The adaptation of foundation models has significantly advanced environmental audio deepfake detection (EADD), a rapidly growing area of research. These models are typically fine-tu…
Avengers Assemble: Amalgamation of Non-Semantic Features for Depression Detection
Orchid Chetia Phukan, Swarup Ranjan Behera, Shubham Singh +5
In this study, we address the challenge of depression detection from speech, focusing on the potential of non-semantic features (NSFs) to capture subtle markers of depression. Whil…
Strong Alone, Stronger Together: Synergizing Modality-Binding Foundation Models with Optimal Transport for Non-Verbal Emotion Recognition
Orchid Chetia Phukan, Mohd Mujtaba Akhtar, Girish +5
In this study, we investigate multimodal foundation models (MFMs) for emotion recognition from non-verbal sounds. We hypothesize that MFMs, with their joint pre-training across mul…
Are Music Foundation Models Better at Singing Voice Deepfake Detection? Far-Better Fuse them with Speech Foundation Models
Orchid Chetia Phukan, Sarthak Jain, Swarup Ranjan Behera +3
In this study, for the first time, we extensively investigate whether music foundation models (MFMs) or speech foundation models (SFMs) work better for singing voice deepfake detec…
AVR: Synergizing Foundation Models for Audio-Visual Humor Detection
Sarthak Sharma, Orchid Chetia Phukan, Drishti Singh +2
In this work, we present, AVR application for audio-visual humor detection. While humor detection has traditionally centered around textual analysis, recent advancements have spotl…
Towards Multilingual Audio-Visual Question Answering
Orchid Chetia Phukan, Priyabrata Mallick, Swarup Ranjan Behera +3
In this paper, we work towards extending Audio-Visual Question Answering (AVQA) to multilingual settings. Existing AVQA research has predominantly revolved around English and repli…
The Reasonable Effectiveness of Speaker Embeddings for Violence Detection
Sarthak Jain, Orchid Chetia Phukan, Arun Balaji Buduru +1
In this paper, we focus on audio violence detection (AVD). AVD is necessary for several reasons, especially in the context of maintaining safety, preventing harm, and ensuring secu…
PERSONA: An Application for Emotion Recognition, Gender Recognition and Age Estimation
Devyani Koshal, Orchid Chetia Phukan, Sarthak Jain +2
Emotion Recognition (ER), Gender Recognition (GR), and Age Estimation (AE) constitute paralinguistic tasks that rely not on the spoken content but primarily on speech characteristi…
ComFeAT: Combination of Neural and Spectral Features for Improved Depression Detection
Orchid Chetia Phukan, Sarthak Jain, Shubham Singh +3
In this work, we focus on the detection of depression through speech analysis. Previous research has widely explored features extracted from pre-trained models (PTMs) primarily tra…
CoLLAB: A Collaborative Approach for Multilingual Abuse Detection
Orchid Chetia Phukan, Yashasvi Chaurasia, Arun Balaji Buduru +1
In this study, we investigate representations from paralingual Pre-Trained model (PTM) for Audio Abuse Detection (AAD), which has not been explored for AAD. Our results demonstrate…
SONIC: Synergizing VisiON Foundation Models for Stress RecogNItion from ECG signals
Orchid Chetia Phukan, Ankita Das, Arun Balaji Buduru +1
Stress recognition through physiological signals such as Electrocardiogram (ECG) signals has garnered significant attention. Traditionally, research in this field predominantly foc…
Heterogeneity over Homogeneity: Investigating Multilingual Speech Pre-Trained Models for Detecting Audio Deepfake
Orchid Chetia Phukan, Gautam Siddharth Kashyap, Arun Balaji Buduru +1
In this work, we investigate multilingual speech Pre-Trained models (PTMs) for Audio deepfake detection (ADD). We hypothesize that multilingual PTMs trained on large-scale diverse…
Are Paralinguistic Representations all that is needed for Speech Emotion Recognition?
Orchid Chetia Phukan, Gautam Siddharth Kashyap, Arun Balaji Buduru +1
Availability of representations from pre-trained models (PTMs) have facilitated substantial progress in speech emotion recognition (SER). Particularly, representations from PTM tra…