Search Results for author: Sriram Ganapathy

Found 36 papers, 15 papers with code

Svadhyaya system for the Second Diagnosing COVID-19 using Acoustics Challenge 2021

no code implementations11 Jun 2022 Deepak Mittal, Amir H. Poorjam, Debottam Dutta, Debarpan Bhattacharya, Zemin Yu, Sriram Ganapathy, Maneesh Singh

This report describes the system used for detecting COVID-19 positives using three different acoustic modalities, namely speech, breathing, and cough in the second DiCOVA challenge.

The Second DiCOVA Challenge: Dataset and performance analysis for COVID-19 diagnosis using acoustics

no code implementations4 Oct 2021 Neeraj Kumar Sharma, Srikanth Raj Chetupalli, Debarpan Bhattacharya, Debottam Dutta, Pravin Mote, Sriram Ganapathy

This paper presents the details of the challenge, which was an open call for researchers to analyze a dataset of audio recordings consisting of breathing, cough and speech signals.

COVID-19 Diagnosis

Self-Supervised Metric Learning With Graph Clustering For Speaker Diarization

1 code implementation14 Sep 2021 Prachi Singh, Sriram Ganapathy

In this paper, we propose an approach that jointly learns the speaker embeddings and the similarity metric using principles of self-supervised learning.

Graph Clustering Metric Learning +4

A Multi-Head Relevance Weighting Framework For Learning Raw Waveform Audio Representations

no code implementations30 Jul 2021 Debottam Dutta, Purvi Agrawal, Sriram Ganapathy

The relevance weighted representations are fed to a neural classifier and the whole system is trained jointly for the audio classification objective.

Audio Classification

Towards sound based testing of COVID-19 -- Summary of the first Diagnostics of COVID-19 using Acoustics (DiCOVA) Challenge

no code implementations21 Jun 2021 Neeraj Kumar Sharma, Ananya Muguli, Prashant Krishnan, Rohit Kumar, Srikanth Raj Chetupalli, Sriram Ganapathy

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

1 code implementation1 Jun 2021 Srikanth Raj Chetupalli, Prashant Krishnan, Neeraj Sharma, Ananya Muguli, Rohit Kumar, Viral Nanda, Lancelot Mark Pinto, Prasanta Kumar Ghosh, Sriram Ganapathy

The research direction of identifying acoustic bio-markers of respiratory diseases has received renewed interest following the onset of COVID-19 pandemic.

Deep Correlation Analysis for Audio-EEG Decoding

no code implementations18 May 2021 Jaswanth Reddy Katthi, Sriram Ganapathy

A deep model is proposed for intra-subject audio-EEG analysis based on directly optimizing the correlation loss.

Eeg Decoding

Self-supervised Representation Learning With Path Integral Clustering For Speaker Diarization

1 code implementation19 Apr 2021 Prachi Singh, Sriram Ganapathy

In this paper, we propose a representation learning and clustering algorithm that can be iteratively performed for improved speaker diarization.

Representation Learning Self-Supervised Learning +2

LEAP Submission for the Third DIHARD Diarization Challenge

no code implementations6 Apr 2021 Prachi Singh, Rajat Varma, Venkat Krishnamohan, Srikanth Raj Chetupalli, Sriram Ganapathy

This paper describes the challenge submission, the post-evaluation analysis and improvements observed on the DIHARD-III dataset.

speaker-diarization Speaker Diarization

Speaker conditioned acoustic modeling for multi-speaker conversational ASR

no code implementations5 Apr 2021 Srikanth Raj Chetupalli, Sriram Ganapathy

The proposed model is a combination of a speaker diarization system and a hybrid automatic speech recognition (ASR) system.

Automatic Speech Recognition speaker-diarization +2

DiCOVA Challenge: Dataset, task, and baseline system for COVID-19 diagnosis using acoustics

no code implementations16 Mar 2021 Ananya Muguli, Lancelot Pinto, Nirmala R., Neeraj Sharma, Prashant Krishnan, Prasanta Kumar Ghosh, Rohit Kumar, Shrirama Bhat, Srikanth Raj Chetupalli, Sriram Ganapathy, Shreyas Ramoji, Viral Nanda

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.

COVID-19 Diagnosis

Deep Multiway Canonical Correlation Analysis for Multi-Subject EEG Normalization

no code implementations11 Mar 2021 Jaswanth Reddy Katthi, Sriram Ganapathy

The experiments are performed on EEG data collected from subjects listening to natural speech and music.

EEG

End-to-end lyrics Recognition with Voice to Singing Style Transfer

1 code implementation17 Feb 2021 Sakya Basak, Shrutina Agarwal, Sriram Ganapathy, Naoya Takahashi

This approach, called voice to singing (V2S), performs the voice style conversion by modulating the F0 contour of the natural speech with that of a singing voice.

Data Augmentation Language Modelling +2

The Third DIHARD Diarization Challenge

2 code implementations2 Dec 2020 Neville Ryant, Prachi Singh, Venkat Krishnamohan, Rajat Varma, Kenneth Church, Christopher Cieri, Jun Du, Sriram Ganapathy, Mark Liberman

DIHARD III was the third in a series of speaker diarization challenges intended to improve the robustness of diarization systems to variability in recording equipment, noise conditions, and conversational domain.

speaker-diarization Speaker Diarization

Neural PLDA Modeling for End-to-End Speaker Verification

1 code implementation11 Aug 2020 Shreyas Ramoji, Prashant Krishnan, Sriram Ganapathy

Recently, we had proposed a neural network approach for backend modeling in speaker verification called the neural PLDA (NPLDA) where the likelihood ratio score of the generative PLDA model is posed as a discriminative similarity function and the learnable parameters of the score function are optimized using a verification cost.

Speaker Recognition Speaker Verification

Deep Self-Supervised Hierarchical Clustering for Speaker Diarization

1 code implementation10 Aug 2020 Prachi Singh, Sriram Ganapathy

In this paper, we propose a novel algorithm for hierarchical clustering which combines the speaker clustering along with a representation learning framework.

Audio and Speech Processing

Deep Learning Based Dereverberation of Temporal Envelopesfor Robust Speech Recognition

no code implementations7 Aug 2020 Anurenjan Purushothaman, Anirudh Sreeram, Rohit Kumar, Sriram Ganapathy

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 Robust Speech Recognition +1

NISP: A Multi-lingual Multi-accent Dataset for Speaker Profiling

1 code implementation12 Jul 2020 Shareef Babu Kalluri, Deepu Vijayasenan, Sriram Ganapathy, Ragesh Rajan M, Prashant Krishnan

The metadata information for speaker profiling applications like linguistic information, regional information, and physical characteristics of a speaker are also collected.

Speaker Profiling

Towards Relevance and Sequence Modeling in Language Recognition

no code implementations2 Apr 2020 Bharat Padi, Anand Mohan, Sriram Ganapathy

In particular, a new model is proposed for incorporating relevance in language recognition, where parts of speech data are weighted more based on their relevance for the language recognition task.

Language Identification Speaker Recognition

NPLDA: A Deep Neural PLDA Model for Speaker Verification

1 code implementation10 Feb 2020 Shreyas Ramoji, Prashant Krishnan, Sriram Ganapathy

The likelihood ratio score of the generative PLDA model is posed as a discriminative similarity function and the learnable parameters of the score function are optimized using a verification cost.

Speaker Recognition Speaker Verification

LEAP System for SRE19 CTS Challenge -- Improvements and Error Analysis

no code implementations7 Feb 2020 Shreyas Ramoji, Prashant Krishnan, Bhargavram Mysore, Prachi Singh, Sriram Ganapathy

In this paper, we provide a detailed account of the LEAP SRE system submitted to the CTS challenge focusing on the novel components in the back-end system modeling.

Speaker Recognition Speaker Verification

Pairwise Discriminative Neural PLDA for Speaker Verification

1 code implementation20 Jan 2020 Shreyas Ramoji, Prashant Krishnan V, Prachi Singh, Sriram Ganapathy

The pre-processing steps of linear discriminant analysis (LDA), unit length normalization and within class covariance normalization are all modeled as layers of a neural model and the speaker verification cost functions can be back-propagated through these layers during training.

Speaker Verification

Improving Voice Separation by Incorporating End-to-end Speech Recognition

1 code implementation29 Nov 2019 Naoya Takahashi, Mayank Kumar Singh, Sakya Basak, Parthasaarathy Sudarsanam, Sriram Ganapathy, Yuki Mitsufuji

Despite recent advances in voice separation methods, many challenges remain in realistic scenarios such as noisy recording and the limits of available data.

Automatic Speech Recognition speech-recognition +2

3-D Feature and Acoustic Modeling for Far-Field Speech Recognition

no code implementations13 Nov 2019 Anurenjan Purushothaman, Anirudh Sreeram, Sriram Ganapathy

The MAR features are fed to a convolutional neural network (CNN) architecture which performs the joint acoustic modeling on the three dimensions.

Automatic Speech Recognition speech-recognition

The Second DIHARD Diarization Challenge: Dataset, task, and baselines

1 code implementation18 Jun 2019 Neville Ryant, Kenneth Church, Christopher Cieri, Alejandrina Cristia, Jun Du, Sriram Ganapathy, Mark Liberman

This paper introduces the second DIHARD challenge, the second in a series of speaker diarization challenges intended to improve the robustness of diarization systems to variation in recording equipment, noise conditions, and conversational domain.

Action Detection Activity Detection +4

Leveraging Native Language Speech for Accent Identification using Deep Siamese Networks

no code implementations25 Dec 2017 Aditya Siddhant, Preethi Jyothi, Sriram Ganapathy

The problem of automatic accent identification is important for several applications like speaker profiling and recognition as well as for improving speech recognition systems.

Association Speaker Profiling +2

The IBM Speaker Recognition System: Recent Advances and Error Analysis

no code implementations5 May 2016 Seyed Omid Sadjadi, Jason Pelecanos, Sriram Ganapathy

We present the recent advances along with an error analysis of the IBM speaker recognition system for conversational speech.

Automatic Speech Recognition Speaker Recognition +1

The IBM 2016 Speaker Recognition System

no code implementations23 Feb 2016 Seyed Omid Sadjadi, Sriram Ganapathy, Jason W. Pelecanos

In this paper we describe the recent advancements made in the IBM i-vector speaker recognition system for conversational speech.

Automatic Speech Recognition Speaker Recognition +1

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