Search Results for author:
Found 36 papers, 15 papers with code
Leveraging Symmetrical Convolutional Transformer Networks for Speech to Singing Voice Style Transfer
In this paper, we propose a model to perform style transfer of speech to singing voice.
Interpretable Acoustic Representation Learning on Breathing and Speech Signals for COVID-19 Detection
In this paper, we describe an approach for representation learning of audio signals for the task of COVID-19 detection.
Analyzing the impact of SARS-CoV-2 variants on respiratory sound signals
The COVID-19 outbreak resulted in multiple waves of infections that have been associated with different SARS-CoV-2 variants.
Svadhyaya system for the Second Diagnosing COVID-19 using Acoustics Challenge 2021
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.
Coswara: A website application enabling COVID-19 screening by analysing respiratory sound samples and health symptoms
The COVID-19 pandemic has accelerated research on design of alternative, quick and effective COVID-19 diagnosis approaches.
The Second DiCOVA Challenge: Dataset and performance analysis for COVID-19 diagnosis using acoustics
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.
Self-Supervised Metric Learning With Graph Clustering For Speaker Diarization
In this paper, we propose an approach that jointly learns the speaker embeddings and the similarity metric using principles of self-supervised learning.
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.
A Multi-Head Relevance Weighting Framework For Learning Raw Waveform Audio Representations
The relevance weighted representations are fed to a neural classifier and the whole system is trained jointly for the audio classification objective.
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.
Deep Correlation Analysis for Audio-EEG Decoding
A deep model is proposed for intra-subject audio-EEG analysis based on directly optimizing the correlation loss.
Self-supervised Representation Learning With Path Integral Clustering For Speaker Diarization
In this paper, we propose a representation learning and clustering algorithm that can be iteratively performed for improved speaker diarization.
LEAP Submission for the Third DIHARD Diarization Challenge
This paper describes the challenge submission, the post-evaluation analysis and improvements observed on the DIHARD-III dataset.
Speaker conditioned acoustic modeling for multi-speaker conversational ASR
The proposed model is a combination of a speaker diarization system and a hybrid automatic speech recognition (ASR) system.
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.
Deep Multiway Canonical Correlation Analysis for Multi-Subject EEG Normalization
The experiments are performed on EEG data collected from subjects listening to natural speech and music.
End-to-end lyrics Recognition with Voice to Singing Style Transfer
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.
The Third DIHARD Diarization Challenge
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.
Neural PLDA Modeling for End-to-End Speaker Verification
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.
Deep Self-Supervised Hierarchical Clustering for Speaker Diarization
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
Automatic speech recognition in reverberant conditions is a challenging task as the long-term envelopes of the reverberant speech are temporally smeared.
NISP: A Multi-lingual Multi-accent Dataset for Speaker Profiling
The metadata information for speaker profiling applications like linguistic information, regional information, and physical characteristics of a speaker are also collected.
Towards Relevance and Sequence Modeling in Language Recognition
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.
NPLDA: A Deep Neural PLDA Model for Speaker Verification
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.
LEAP System for SRE19 CTS Challenge -- Improvements and Error Analysis
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.
Pairwise Discriminative Neural PLDA for Speaker Verification
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.
Improving Voice Separation by Incorporating End-to-end Speech Recognition
Despite recent advances in voice separation methods, many challenges remain in realistic scenarios such as noisy recording and the limits of available data.
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).
3-D Feature and Acoustic Modeling for Far-Field Speech Recognition
The MAR features are fed to a convolutional neural network (CNN) architecture which performs the joint acoustic modeling on the three dimensions.
The Second DIHARD Diarization Challenge: Dataset, task, and baselines
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.
Leveraging Native Language Speech for Accent Identification using Deep Siamese Networks
The problem of automatic accent identification is important for several applications like speaker profiling and recognition as well as for improving speech recognition systems.
The IBM Speaker Recognition System: Recent Advances and Error Analysis
We present the recent advances along with an error analysis of the IBM speaker recognition system for conversational speech.
The IBM 2016 Speaker Recognition System
In this paper we describe the recent advancements made in the IBM i-vector speaker recognition system for conversational speech.
