Search Results for author: Rajesh Sharma

Found 39 papers, 8 papers with code

ProvocationProbe: Instigating Hate Speech Dataset from Twitter

no code implementations25 Oct 2024 Abhay Kumar, Vigneshwaran Shankaran, Rajesh Sharma

In the recent years online social media platforms has been flooded with hateful remarks such as racism, sexism, homophobia etc.

Beyond Speech and More: Investigating the Emergent Ability of Speech Foundation Models for Classifying Physiological Time-Series Signals

no code implementations16 Oct 2024 Orchid Chetia Phukan, Swarup Ranjan Behera, Girish, Mohd Mujtaba Akhtar, Arun Balaji Buduru, Rajesh Sharma

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.

Audio Classification Time Series

Multi-View Multi-Task Modeling with Speech Foundation Models for Speech Forensic Tasks

no code implementations16 Oct 2024 Orchid Chetia Phukan, Devyani Koshal, Swarup Ranjan Behera, Arun Balaji Buduru, Rajesh Sharma

However, most prior efforts have centered on building individual models for each task separately, despite the inherent similarities among these tasks.

Age Estimation Multi-Task Learning +3

Strong Alone, Stronger Together: Synergizing Modality-Binding Foundation Models with Optimal Transport for Non-Verbal Emotion Recognition

no code implementations21 Sep 2024 Orchid Chetia Phukan, Mohd Mujtaba Akhtar, Girish, Swarup Ranjan Behera, Sishir Kalita, Arun Balaji Buduru, Rajesh Sharma, S. R Mahadeva Prasanna

Through MATA coupled with the combination of MFMs: LanguageBind and ImageBind, we report the topmost performance with accuracies of 76. 47%, 77. 40%, 75. 12% and F1-scores of 70. 35%, 76. 19%, 74. 63% for ASVP-ESD, JNV, and VIVAE datasets against individual FMs and baseline fusion techniques and report SOTA on the benchmark datasets.

Audio Deepfake Detection DeepFake Detection +4

Are Music Foundation Models Better at Singing Voice Deepfake Detection? Far-Better Fuse them with Speech Foundation Models

no code implementations21 Sep 2024 Orchid Chetia Phukan, Sarthak Jain, Swarup Ranjan Behera, Arun Balaji Buduru, Rajesh Sharma, S. R Mahadeva Prasanna

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 detection (SVDD), which has recently attracted attention in the research community.

DeepFake Detection Face Swapping +3

A Fine-grained Sentiment Analysis of App Reviews using Large Language Models: An Evaluation Study

1 code implementation11 Sep 2024 Faiz Ali Shah, Ahmed Sabir, Rajesh Sharma

Given the volume of user reviews received daily, an automated mechanism to generate feature-level sentiment summaries of user reviews is needed.

Sentiment Analysis

ComFeAT: Combination of Neural and Spectral Features for Improved Depression Detection

no code implementations10 Jun 2024 Orchid Chetia Phukan, Sarthak Jain, Shubham Singh, Muskaan Singh, Arun Balaji Buduru, Rajesh Sharma

To address this, in this paper, we introduce ComFeAT, an application that employs a CNN model trained on a combination of features extracted from PTMs, a. k. a.

Depression Detection

PERSONA: An Application for Emotion Recognition, Gender Recognition and Age Estimation

no code implementations10 Jun 2024 Devyani Koshal, Orchid Chetia Phukan, Sarthak Jain, Arun Balaji Buduru, Rajesh Sharma

Emotion Recognition (ER), Gender Recognition (GR), and Age Estimation (AE) constitute paralinguistic tasks that rely not on the spoken content but primarily on speech characteristics such as pitch and tone.

Age Estimation Emotion Recognition +2

CoLLAB: A Collaborative Approach for Multilingual Abuse Detection

no code implementations5 Jun 2024 Orchid Chetia Phukan, Yashasvi Chaurasia, Arun Balaji Buduru, Rajesh Sharma

In this study, we investigate representations from paralingual Pre-Trained model (PTM) for Audio Abuse Detection (AAD), which has not been explored for AAD.

Abuse Detection

Analyzing Toxicity in Deep Conversations: A Reddit Case Study

no code implementations11 Apr 2024 Vigneshwaran Shankaran, Rajesh Sharma

This anonymity has also made social media prone to harmful content, which requires moderation to ensure responsible and productive use.

SONIC: Synergizing VisiON Foundation Models for Stress RecogNItion from ECG signals

no code implementations31 Mar 2024 Orchid Chetia Phukan, Ankita Das, Arun Balaji Buduru, Rajesh Sharma

Stress recognition through physiological signals such as Electrocardiogram (ECG) signals has garnered significant attention.

Heterogeneity over Homogeneity: Investigating Multilingual Speech Pre-Trained Models for Detecting Audio Deepfake

1 code implementation31 Mar 2024 Orchid Chetia Phukan, Gautam Siddharth Kashyap, Arun Balaji Buduru, Rajesh Sharma

To validate our hypothesis, we extract representations from state-of-the-art (SOTA) PTMs including monolingual, multilingual as well as PTMs trained for speaker and emotion recognition, and evaluated them on ASVSpoof 2019 (ASV), In-the-Wild (ITW), and DECRO benchmark databases.

Audio Deepfake Detection DeepFake Detection +2

Revisiting The Classics: A Study on Identifying and Rectifying Gender Stereotypes in Rhymes and Poems

1 code implementation18 Mar 2024 Aditya Narayan Sankaran, Vigneshwaran Shankaran, Sampath Lonka, Rajesh Sharma

To summarize, this work highlights the pervasive nature of gender stereotypes in literary works and reveals the potential of LLMs to rectify gender stereotypes.

Language Modelling Large Language Model

Are Paralinguistic Representations all that is needed for Speech Emotion Recognition?

no code implementations2 Feb 2024 Orchid Chetia Phukan, Gautam Siddharth Kashyap, Arun Balaji Buduru, Rajesh Sharma

However, such paralinguistic PTM representations haven't been evaluated for SER in linguistic environments other than English.

Speech Emotion Recognition

A Survey on Online User Aggression: Content Detection and Behavioural Analysis on Social Media Platforms

no code implementations15 Nov 2023 Swapnil Mane, Suman Kundu, Rajesh Sharma

Recognizing the societal risks associated with unchecked aggressive content, this paper delves into the field of Aggression Content Detection and Behavioral Analysis of Aggressive Users, aiming to bridge the gap between disparate studies.

feature selection Systematic Literature Review

AMIR: Automated MisInformation Rebuttal -- A COVID-19 Vaccination Datasets based Recommendation System

no code implementations29 Oct 2023 Shakshi Sharma, Anwitaman Datta, Rajesh Sharma

While the ideas herein can be generalized and reapplied in the broader context of misinformation mitigation using a multitude of information sources and catering to the spectrum of social media platforms, this work serves as a proof of concept, and as such, it is confined in its scope to only rebuttal of tweets, and in the specific context of misinformation regarding COVID-19.

Misinformation

Reinforcement Learning-based Knowledge Graph Reasoning for Explainable Fact-checking

no code implementations11 Oct 2023 Gustav Nikopensius, Mohit Mayank, Orchid Chetia Phukan, Rajesh Sharma

Extensive experiments on FB15K-277 and NELL-995 datasets reveal that reasoning over a KG is an effective way of producing human-readable explanations in the form of paths and classifications for fact claims.

Fact Checking Misinformation +3

Misinformation Concierge: A Proof-of-Concept with Curated Twitter Dataset on COVID-19 Vaccination

no code implementations25 Aug 2023 Shakshi Sharma, Anwitaman Datta, Vigneshwaran Shankaran, Rajesh Sharma

We demonstrate the Misinformation Concierge, a proof-of-concept that provides actionable intelligence on misinformation prevalent in social media.

Misinformation

minOffense: Inter-Agreement Hate Terms for Stable Rules, Concepts, Transitivities, and Lattices

no code implementations29 May 2023 Animesh Chaturvedi, Rajesh Sharma

For a given set of Hate Terms lists (HTs-lists) and Hate Speech data (HS-data), it is challenging to understand which hate term contributes the most for hate speech classification.

Classification

A Comparative Study of Pre-trained Speech and Audio Embeddings for Speech Emotion Recognition

no code implementations22 Apr 2023 Orchid Chetia Phukan, Arun Balaji Buduru, Rajesh Sharma

In this work, we exploit this research gap and perform a comparative analysis of embeddings extracted from eight speech and audio PTMs (wav2vec 2. 0, data2vec, wavLM, UniSpeech-SAT, wav2clip, YAMNet, x-vector, ECAPA).

Speaker Recognition Speech Emotion Recognition

Predicting Socio-Economic Well-being Using Mobile Apps Data: A Case Study of France

no code implementations15 Jan 2023 Rahul Goel, Angelo Furno, Rajesh Sharma

Nonetheless, alternative data sources, such as call data records (CDR) and mobile app usage, can serve as cost-effective and up-to-date sources for identifying socio-economic indicators.

GAME-ON: Graph Attention Network based Multimodal Fusion for Fake News Detection

1 code implementation25 Feb 2022 Mudit Dhawan, Shakshi Sharma, Aditya Kadam, Rajesh Sharma, Ponnurangam Kumaraguru

A plethora of previous multimodal-based work has tried to address the problem of modeling heterogeneous modalities in identifying fake content.

Fake News Detection Graph Attention +1

What goes on inside rumour and non-rumour tweets and their reactions: A Psycholinguistic Analyses

no code implementations9 Nov 2021 Sabur Butt, Shakshi Sharma, Rajesh Sharma, Grigori Sidorov, Alexander Gelbukh

In the descriptive line of works, where researchers have tried to analyse rumours using NLP approaches, there isnt much emphasis on psycho-linguistics analyses of social media text.

Descriptive Misinformation +1

DEAP-FAKED: Knowledge Graph based Approach for Fake News Detection

no code implementations4 Jul 2021 Mohit Mayank, Shakshi Sharma, Rajesh Sharma

Our approach is a combination of the NLP -- where we encode the news content, and the GNN technique -- where we encode the Knowledge Graph (KG).

Fake News Detection

Misinformation Detection on YouTube Using Video Captions

1 code implementation2 Jul 2021 Raj Jagtap, Abhinav Kumar, Rahul Goel, Shakshi Sharma, Rajesh Sharma, Clint P. George

Using caption dataset, the proposed models can classify videos among three classes (Misinformation, Debunking Misinformation, and Neutral) with 0. 85 to 0. 90 F1-score.

Misinformation

COVID-19 and the stock market: evidence from Twitter

no code implementations13 Nov 2020 Rahul Goel, Lucas Javier Ford, Maksym Obrizan, Rajesh Sharma

COVID-19 has had a much larger impact on the financial markets compared to previous epidemics because the news information is transferred over the social networks at a speed of light.

Identifying Possible Rumor Spreaders on Twitter: A Weak Supervised Learning Approach

2 code implementations15 Oct 2020 Shakshi Sharma, Rajesh Sharma

Thus, it is important to detect and control the misinformation in such platforms before it spreads to the masses.

Graph Neural Network Misinformation

Which bills are lobbied? Predicting and interpreting lobbying activity in the US

no code implementations29 Apr 2020 Ivan Slobozhan, Peter Ormosi, Rajesh Sharma

We also investigate the influence of the intensity of the lobbying activity on how discernible a lobbied bill is from one that was not subject to lobbying.

Identifying Semantically Duplicate Questions Using Data Science Approach: A Quora Case Study

no code implementations18 Apr 2020 Navedanjum Ansari, Rajesh Sharma

Three out of four proposed architectures outperformed the accuracy from previous machine learning and deep learning research work, two out of four models outperformed accuracy from previous deep learning study on Quora's question pair dataset, and our best model achieved accuracy of 85. 82% which is close to Quora state of the art accuracy.

BIG-bench Machine Learning Deep Learning +3

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