In this paper, we propose a model to perform style transfer of speech to singing voice.
In this paper, we describe an approach for representation learning of audio signals for the task of COVID-19 detection.
no code implementations • 24 Jun 2022 • Debarpan Bhattacharya, Debottam Dutta, Neeraj Kumar Sharma, Srikanth Raj Chetupalli, Pravin Mote, Sriram Ganapathy, Chandrakiran C, Sahiti Nori, Suhail K K, Sadhana Gonuguntla, Murali Alagesan
The COVID-19 outbreak resulted in multiple waves of infections that have been associated with different SARS-CoV-2 variants.
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.
1 code implementation • 9 Jun 2022 • Debarpan Bhattacharya, Debottam Dutta, Neeraj Kumar Sharma, Srikanth Raj Chetupalli, Pravin Mote, Sriram Ganapathy, Chandrakiran C, Sahiti Nori, Suhail K K, Sadhana Gonuguntla, Murali Alagesan
The COVID-19 pandemic has accelerated research on design of alternative, quick and effective COVID-19 diagnosis approaches.
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.
In this paper, we propose an approach that jointly learns the speaker embeddings and the similarity metric using principles of self-supervised learning.
The dereverberated envelopes are used for feature extraction in speech recognition.
In this paper, we develop a feature enhancement approach using a neural model operating on sub-band temporal envelopes.
The relevance weighted representations are fed to a neural classifier and the whole system is trained jointly for the audio classification objective.
This paper presents the details of the SRIB-LEAP submission to the ConferencingSpeech challenge 2021.
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.
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 propose a representation learning and clustering algorithm that can be iteratively performed for improved speaker diarization.
This paper describes the challenge submission, the post-evaluation analysis and improvements observed on the DIHARD-III dataset.
The proposed model is a combination of a speaker diarization system and a hybrid automatic speech recognition (ASR) system.
no code implementations • 16 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.
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.
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.
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.
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
Automatic speech recognition in reverberant conditions is a challenging task as the long-term envelopes of the reverberant speech are temporally smeared.
The metadata information for speaker profiling applications like linguistic information, regional information, and physical characteristics of a speaker are also collected.
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.
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.
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.
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.
Despite recent advances in voice separation methods, many challenges remain in realistic scenarios such as noisy recording and the limits of available data.
These models are trained using a paired corpus of clean and noisy recordings (teacher model).
The MAR features are fed to a convolutional neural network (CNN) architecture which performs the joint acoustic modeling on the three dimensions.
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.
The problem of automatic accent identification is important for several applications like speaker profiling and recognition as well as for improving speech recognition systems.
We present the recent advances along with an error analysis of the IBM speaker recognition system for conversational speech.
In this paper we describe the recent advancements made in the IBM i-vector speaker recognition system for conversational speech.