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Found 16 papers, 2 papers with code
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.
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.
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.
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.
How Paralingual are Paralinguistic Representations? A Case Study in Speech Emotion Recognition
We also show that downstream models using TRILLsson representations achieve SOTA performance in terms of accuracy across various multi-lingual datasets.
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).
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.
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.
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.
