341 citations · 356 across the 23 of their papers we have counts for
27 papers
Timestamped Hindi speech transcription using Whisper
Sanat Kumar Agrawal, Srikanth Raj Chetupalli
Time accurate speech transcription is essential in critical appli- cations, such as medical conversation transcription, reading as- sessment, and atypical speech recognition. Encod…
Indic-CLAP: Text--Audio Understanding in Indic Languages via Cross-Lingual Distillation
Sanat Kumar Agrawal, Srikanth Raj Chetupalli
Contrastive Language--Audio Pretraining (CLAP) learns a joint text--audio representation space, which enables zero-shot audio understanding from natural-language descriptions. Howe…
Benchmarking Speech Systems for Frontline Health Conversations: The DISPLACE-M Challenge
Dhanya E, Ankita Meena, Manas Nanivadekar +11
The DIarization and Speech Processing for LAnguage understanding in Conversational Environments - Medical (DISPLACE-M) challenge introduces a conversational AI benchmark for unders…
Neural Directional Filtering Using a Compact Microphone Array
Weilong Huang, Srikanth Raj Chetupalli, Mhd Modar Halimeh +2
Beamforming with desired directivity patterns using compact microphone arrays is essential in many audio applications. Directivity patterns achievable using traditional beamformers…
Neural Directional Filtering with Configurable Directivity Pattern at Inference
Weilong Huang, Srikanth Raj Chetupalli, Emanuël A. P. Habets
Spatial filtering with a desired directivity pattern is advantageous for many audio applications. In this work, we propose neural directional filtering with user-defined directivit…
Training Strategies for Modality Dropout Resilient Multi-Modal Target Speaker Extraction
Srikanth Korse, Mohamed Elminshawi, Emanuel A. P. Habets +1
The primary goal of multi-modal TSE (MTSE) is to extract a target speaker from a speech mixture using complementary information from different modalities, such as audio enrolment a…
Dynamic Slimmable Networks for Efficient Speech Separation
Mohamed Elminshawi, Srikanth Raj Chetupalli, Emanuël A. P. Habets
Recent progress in speech separation has been largely driven by advances in deep neural networks, yet their high computational and memory requirements hinder deployment on resource…
Low-Complexity Neural Wind Noise Reduction for Audio Recordings
Hesam Eftekhari, Srikanth Raj Chetupalli, Shrishti Saha Shetu +2
Wind noise significantly degrades the quality of outdoor audio recordings, yet remains difficult to suppress in real-time on resource-constrained devices. In this work, we propose…
Neural Directional Filtering: Far-Field Directivity Control With a Small Microphone Array
Julian Wechsler, Srikanth Raj Chetupalli, Mhd Modar Halimeh +2
Capturing audio signals with specific directivity patterns is essential in speech communication. This study presents a deep neural network (DNN)-based approach to directional filte…
Coswara: A respiratory sounds and symptoms dataset for remote screening of SARS-CoV-2 infection
Debarpan Bhattacharya, Neeraj Kumar Sharma, Debottam Dutta +8
This paper presents the Coswara dataset, a dataset containing diverse set of respiratory sounds and rich meta-data, recorded between April-2020 and February-2022 from 2635 individu…
Beamformer-Guided Target Speaker Extraction
Mohamed Elminshawi, Srikanth Raj Chetupalli, Emanuël A. P. Habets
We propose a Beamformer-guided Target Speaker Extraction (BG-TSE) method to extract a target speaker's voice from a multi-channel recording informed by the direction of arrival of…
Multi-Microphone Speaker Separation by Spatial Regions
Julian Wechsler, Srikanth Raj Chetupalli, Wolfgang Mack +1
We consider the task of region-based source separation of reverberant multi-microphone recordings. We assume pre-defined spatial regions with a single active source per region. The…
Analyzing the impact of SARS-CoV-2 variants on respiratory sound signals
Debarpan Bhattacharya, Debottam Dutta, Neeraj Kumar Sharma +8
The COVID-19 outbreak resulted in multiple waves of infections that have been associated with different SARS-CoV-2 variants. Studies have reported differential impact of the varian…
AmbiSep: Ambisonic-to-Ambisonic Reverberant Speech Separation Using Transformer Networks
Adrian Herzog, Srikanth Raj Chetupalli, Emanuël A. P. Habets
Consider a multichannel Ambisonic recording containing a mixture of several reverberant speech signals. Retreiving the reverberant Ambisonic signals corresponding to the individual…
Coswara: A website application enabling COVID-19 screening by analysing respiratory sound samples and health symptoms
Debarpan Bhattacharya, Debottam Dutta, Neeraj Kumar Sharma +8
The COVID-19 pandemic has accelerated research on design of alternative, quick and effective COVID-19 diagnosis approaches. In this paper, we describe the Coswara tool, a website a…
The Second DiCOVA Challenge: Dataset and performance analysis for COVID-19 diagnosis using acoustics
Neeraj Kumar Sharma, Srikanth Raj Chetupalli, Debarpan Bhattacharya +3
The Second Diagnosis of COVID-19 using Acoustics (DiCOVA) Challenge aimed at accelerating the research in acoustics based detection of COVID-19, a topic at the intersection of acou…
Directional MCLP Analysis and Reconstruction for Spatial Speech Communication
Srikanth Raj Chetupalli, Thippur V. Sreenivas
Spatial speech communication, i.e., the reconstruction of spoken signal along with the relative speaker position in the enclosure (reverberation information) is considered in this…
Towards sound based testing of COVID-19 -- Summary of the first Diagnostics of COVID-19 using Acoustics (DiCOVA) Challenge
Neeraj Kumar Sharma, Ananya Muguli, Prashant Krishnan +3
The technology development for point-of-care tests (POCTs) targeting respiratory diseases has witnessed a growing demand in the recent past. Investigating the presence of acoustic…
Multi-modal Point-of-Care Diagnostics for COVID-19 Based On Acoustics and Symptoms
Srikanth Raj Chetupalli, Prashant Krishnan, Neeraj Sharma +6
The research direction of identifying acoustic bio-markers of respiratory diseases has received renewed interest following the onset of COVID-19 pandemic. In this paper, we design…
LEAP Submission for the Third DIHARD Diarization Challenge
Prachi Singh, Rajat Varma, Venkat Krishnamohan +2
The LEAP submission for DIHARD-III challenge is described in this paper. The proposed system is composed of a speech bandwidth classifier, and diarization systems fine-tuned for na…
Speaker conditioned acoustic modeling for multi-speaker conversational ASR
Srikanth Raj Chetupalli, Sriram Ganapathy
In this paper, we propose a novel approach for the transcription of speech conversations with natural speaker overlap, from single channel speech recordings. The proposed model is…
DiCOVA Challenge: Dataset, task, and baseline system for COVID-19 diagnosis using acoustics
Ananya Muguli, Lancelot Pinto, Nirmala R. +9
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 d…
Context Dependent RNNLM for Automatic Transcription of Conversations
Srikanth Raj Chetupalli, Sriram Ganapathy
Conversational speech, while being unstructured at an utterance level, typically has a macro topic which provides larger context spanning multiple utterances. The current language…
Coswara -- A Database of Breathing, Cough, and Voice Sounds for COVID-19 Diagnosis
Neeraj Sharma, Prashant Krishnan, Rohit Kumar +5
The COVID-19 pandemic presents global challenges transcending boundaries of country, race, religion, and economy. The current gold standard method for COVID-19 detection is the rev…