Search Results for author:
Found 35 papers, 5 papers with code
FAtNet: Cost-Effective Approach Towards Mitigating the Linguistic Bias in Speaker Verification Systems
Empirical results suggest that frame-attentive embedding can cost-effectively reduce linguistic bias and enhance the usability of baselines.
An Exploratory Study on Temporally Evolving Discussion around Covid-19 using Diachronic Word Embeddings
In this work, we aim to show the topics under discussion, evolution of discussions, change in user sentiment during the pandemic.
Investigating Prosodic Signatures via Speech Pre-Trained Models for Audio Deepfake Source Attribution
This higher performance can be due to its speaker recognition pre-training that enables it for capturing unique prosodic characteristics of the sources in a better way.
Multi-View Multi-Task Modeling with Speech Foundation Models for Speech Forensic Tasks
However, most prior efforts have centered on building individual models for each task separately, despite the inherent similarities among these tasks.
Beyond Speech and More: Investigating the Emergent Ability of Speech Foundation Models for Classifying Physiological Time-Series Signals
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.
SeQuiFi: Mitigating Catastrophic Forgetting in Speech Emotion Recognition with Sequential Class-Finetuning
In this work, we introduce SeQuiFi, a novel approach for mitigating catastrophic forgetting (CF) in speech emotion recognition (SER).
Representation Loss Minimization with Randomized Selection Strategy for Efficient Environmental Fake Audio Detection
We show that such random selection preserves more performance than the SOTA dimensionality reduction techniques while reducing model parameters and inference time by almost over half.
Avengers Assemble: Amalgamation of Non-Semantic Features for Depression Detection
In this work, we demonstrate that the amalgamation of NSFs results in complementary behavior, leading to enhanced depression detection performance.
Strong Alone, Stronger Together: Synergizing Modality-Binding Foundation Models with Optimal Transport for Non-Verbal Emotion Recognition
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.
Are Music Foundation Models Better at Singing Voice Deepfake Detection? Far-Better Fuse them with Speech Foundation Models
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.
Towards identifying Source credibility on Information Leakage in Digital Gadget Market
With the digital gadget market becoming highly competitive and ever-evolving, the trend of an increasing number of sensitive posts leaking information on devices in social media is observed.
VoxMed: One-Step Respiratory Disease Classifier using Digital Stethoscope Sounds
As respiratory illnesses become more common, it is crucial to quickly and accurately detect them to improve patient care.
ASGIR: Audio Spectrogram Transformer Guided Classification And Information Retrieval For Birds
Recognition and interpretation of bird vocalizations are pivotal in ornithological research and ecological conservation efforts due to their significance in understanding avian behaviour, performing habitat assessment and judging ecological health.
FGA: Fourier-Guided Attention Network for Crowd Count Estimation
Crowd counting is gaining societal relevance, particularly in domains of Urban Planning, Crowd Management, and Public Safety.
AVR: Synergizing Foundation Models for Audio-Visual Humor Detection
In this work, we present, AVR application for audio-visual humor detection.
Towards Multilingual Audio-Visual Question Answering
In this paper, we work towards extending Audio-Visual Question Answering (AVQA) to multilingual settings.
Audio-visual Question Answering
Audio-Visual Question Answering (AVQA)
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The Reasonable Effectiveness of Speaker Embeddings for Violence Detection
In this paper, we focus on audio violence detection (AVD).
PERSONA: An Application for Emotion Recognition, Gender Recognition and Age Estimation
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.
ComFeAT: Combination of Neural and Spectral Features for Improved Depression Detection
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.
CoLLAB: A Collaborative Approach for Multilingual Abuse Detection
In this study, we investigate representations from paralingual Pre-Trained model (PTM) for Audio Abuse Detection (AAD), which has not been explored for AAD.
BB-Patch: BlackBox Adversarial Patch-Attack using Zeroth-Order Optimization
Most of these adversarial attack strategies assume that the adversary has access to the training data, the model parameters, and the input during deployment, hence, focus on perturbing the pixel level information present in the input image.
SONIC: Synergizing VisiON Foundation Models for Stress RecogNItion from ECG signals
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
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.
Are Paralinguistic Representations all that is needed for Speech Emotion Recognition?
However, such paralinguistic PTM representations haven't been evaluated for SER in linguistic environments other than English.
A Lightweight Feature Fusion Architecture For Resource-Constrained Crowd Counting
However, most of the previous methods rely on a heavy backbone and a complex downstream architecture that restricts the deployment.
Transforming the Embeddings: A Lightweight Technique for Speech Emotion Recognition Tasks
Speech emotion recognition (SER) is a field that has drawn a lot of attention due to its applications in diverse fields.
A Comparative Study of Pre-trained Speech and Audio Embeddings for Speech Emotion Recognition
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).
imdpGAN: Generating Private and Specific Data with Generative Adversarial Networks
With experiments on MNIST dataset, we show that imdpGAN preserves the privacy of the individual data point, and learns latent codes to control the specificity of the generated samples.
Multi-objective Reinforcement Learning based approach for User-Centric Power Optimization in Smart Home Environments
We experiment on real-world smart home data, and show that the multi-objective approaches: i) establish trade-off between the two objectives, ii) achieve better combined user satisfaction and power consumption than single-objective approaches.
Fine-grained Language Identification with Multilingual CapsNet Model
Hence, there is an increasing need for real-time and fine-grained content analysis services, including language identification, content transcription, and analysis.
Making Smart Homes Smarter: Optimizing Energy Consumption with Human in the Loop
Rapid advancements in the Internet of Things (IoT) have facilitated more efficient deployment of smart environment solutions for specific user requirement.
A Survey of Black-Box Adversarial Attacks on Computer Vision Models
Machine learning has seen tremendous advances in the past few years, which has lead to deep learning models being deployed in varied applications of day-to-day life.
Is change the only constant? Profile change perspective on #LokSabhaElections2019
Users on Twitter are identified with the help of their profile attributes that consists of username, display name, profile image, to name a few.
Hashtags are (not) judgemental: The untold story of Lok Sabha elections 2019
In the political context, hashtags on Twitter are used by users to campaign for their parties, spread news, or to get followers and get a general idea by following a discussion built around a hashtag.
An approach to predictively securing critical cloud infrastructures through probabilistic modeling
Cloud infrastructures are being increasingly utilized in critical infrastructures such as banking/finance, transportation and utility management.
