Search Results for author:
Found 19 papers, 5 papers with code
Sub-Resolution mmWave FMCW Radar-based Touch Localization using Deep Learning
In this paper, we propose a millimeter wave (mmWave) radar-based solution to achieve subresolution error performance using a network of four mmWave radar sensors.
TACLE: Task and Class-aware Exemplar-free Semi-supervised Class Incremental Learning
We propose a novel TACLE (TAsk and CLass-awarE) framework to address the relatively unexplored and challenging problem of exemplar-free semi-supervised class incremental learning.
Evaluating the Robustness of Off-Road Autonomous Driving Segmentation against Adversarial Attacks: A Dataset-Centric analysis
This study investigates the vulnerability of semantic segmentation models to adversarial input perturbations, in the domain of off- road autonomous driving.
Whispering LLaMA: A Cross-Modal Generative Error Correction Framework for Speech Recognition
We introduce a new cross-modal fusion technique designed for generative error correction in automatic speech recognition (ASR).
Ranked #4 on
Automatic Speech Recognition (ASR)
on LRS2
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Speech enhancement with frequency domain auto-regressive modeling
The dereverberated envelope-carrier signals are modulated and the sub-band signals are synthesized to reconstruct the audio signal back.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Multilingual Tourist Assistance using ChatGPT: Comparing Capabilities in Hindi, Telugu, and Kannada
This research investigates the effectiveness of ChatGPT, an AI language model by OpenAI, in translating English into Hindi, Telugu, and Kannada languages, aimed at assisting tourists in India's linguistically diverse environment.
Local Relighting of Real Scenes
We introduce the task of local relighting, which changes a photograph of a scene by switching on and off the light sources that are visible within the image.
Dereverberation of Autoregressive Envelopes for Far-field Speech Recognition
The dereverberated envelopes are used for feature extraction in speech recognition.
End-to-End Speech Recognition With Joint Dereverberation Of Sub-Band Autoregressive Envelopes
In this paper, we develop a feature enhancement approach using a neural model operating on sub-band temporal envelopes.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
SRIB-LEAP submission to Far-field Multi-Channel Speech Enhancement Challenge for Video Conferencing
This paper presents the details of the SRIB-LEAP submission to the ConferencingSpeech challenge 2021.
Towards sound based testing of COVID-19 -- Summary of the first Diagnostics of COVID-19 using Acoustics (DiCOVA) Challenge
As part of the challenge, datasets with breathing, cough, and speech sound samples from COVID-19 and non-COVID-19 individuals were released to the participants.
Multi-modal Point-of-Care Diagnostics for COVID-19 Based On Acoustics and Symptoms
The research direction of identifying acoustic bio-markers of respiratory diseases has received renewed interest following the onset of COVID-19 pandemic.
DiCOVA Challenge: Dataset, task, and baseline system for COVID-19 diagnosis using acoustics
The DiCOVA challenge aims at accelerating research in diagnosing COVID-19 using acoustics (DiCOVA), a topic at the intersection of speech and audio processing, respiratory health diagnosis, and machine learning.
Binary Choice with Asymmetric Loss in a Data-Rich Environment: Theory and an Application to Racial Justice
We study the binary choice problem in a data-rich environment with asymmetric loss functions.
Deep Learning Based Dereverberation of Temporal Envelopesfor Robust Speech Recognition
Automatic speech recognition in reverberant conditions is a challenging task as the long-term envelopes of the reverberant speech are temporally smeared.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Unsupervised Neural Mask Estimator For Generalized Eigen-Value Beamforming Based ASR
These models are trained using a paired corpus of clean and noisy recordings (teacher model).
Improving circuit miniaturization and its efficiency using Rough Set Theory
Improving the efficiency, accuracy and low power consumption in an electronic circuit is always been a bottle neck problem.
