no code implementations • 23 Jan 2024 • Prachi Singh, Sriram Ganapathy
Speaker diarization, the task of segmenting an audio recording based on speaker identity, constitutes an important speech pre-processing step for several downstream applications.
1 code implementation • 9 Jan 2024 • Soumya Dutta, Sriram Ganapathy
The problem of audio-to-audio (A2A) style transfer involves replacing the style features of the source audio with those from the target audio while preserving the content related attributes of the source audio.
1 code implementation • 4 Jan 2024 • Rachit Bansal, Bidisha Samanta, Siddharth Dalmia, Nitish Gupta, Shikhar Vashishth, Sriram Ganapathy, Abhishek Bapna, Prateek Jain, Partha Talukdar
Foundational models with billions of parameters which have been trained on large corpora of data have demonstrated non-trivial skills in a variety of domains.
no code implementations • 21 Nov 2023 • Shikha Baghel, Shreyas Ramoji, Somil Jain, Pratik Roy Chowdhuri, Prachi Singh, Deepu Vijayasenan, Sriram Ganapathy
In multi-lingual societies, where multiple languages are spoken in a small geographic vicinity, informal conversations often involve mix of languages.
no code implementations • 2 Nov 2023 • Megh Thakkar, Tolga Bolukbasi, Sriram Ganapathy, Shikhar Vashishth, Sarath Chandar, Partha Talukdar
Once the pre-training corpus has been assembled, all data samples in the corpus are treated with equal importance during LM pre-training.
1 code implementation • 24 Oct 2023 • Darshan Prabhu, Preethi Jyothi, Sriram Ganapathy, Vinit Unni
In this work, we propose a novel accent adaptation approach for end-to-end ASR systems using cross-attention with a trainable set of codebooks.
no code implementations • 24 Sep 2023 • Anurenjan Purushothaman, Debottam Dutta, Rohit Kumar, Sriram Ganapathy
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
no code implementations • 19 Sep 2023 • Shikhar Bharadwaj, Min Ma, Shikhar Vashishth, Ankur Bapna, Sriram Ganapathy, Vera Axelrod, Siddharth Dalmia, Wei Han, Yu Zhang, Daan van Esch, Sandy Ritchie, Partha Talukdar, Jason Riesa
Spoken language identification refers to the task of automatically predicting the spoken language in a given utterance.
no code implementations • 20 Jul 2023 • Anjali Raj, Shikhar Bharadwaj, Sriram Ganapathy, Min Ma, Shikhar Vashishth
In the recent years, speech representation learning is constructed primarily as a self-supervised learning (SSL) task, using the raw audio signal alone, while ignoring the side-information that is often available for a given speech recording.
no code implementations • 14 Jul 2023 • Varun Krishna, Tarun Sai, Sriram Ganapathy
The input to the model consists of audio samples that are windowed and processed with 1-D convolutional layers.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +3
1 code implementation • 1 Jul 2023 • Akshara Soman, Vidhi Sinha, Sriram Ganapathy
Recent studies have shown that the underlying neural mechanisms of human speech comprehension can be analyzed using a match-mismatch classification of the speech stimulus and the neural response.
no code implementations • 7 Jun 2023 • Shikhar Vashishth, Shikhar Bharadwaj, Sriram Ganapathy, Ankur Bapna, Min Ma, Wei Han, Vera Axelrod, Partha Talukdar
In this paper, we propose a novel framework of combining self-supervised representation learning with the language label information for the pre-training task.
1 code implementation • 22 May 2023 • Debarpan Bhattacharya, Neeraj Kumar Sharma, Debottam Dutta, Srikanth Raj Chetupalli, Pravin Mote, Sriram Ganapathy, Chandrakiran C, Sahiti Nori, Suhail K K, Sadhana Gonuguntla, Murali Alagesan
The rich metadata contained demographic information associated with age, gender and geographic location, as well as the health information relating to the symptoms, pre-existing respiratory ailments, comorbidity and SARS-CoV-2 test status.
no code implementations • 14 Apr 2023 • Soumya Dutta, Sriram Ganapathy
The audio and text representations are processed using a set of bi-directional recurrent neural network layers with self-attention that converts each utterance in a given conversation to a fixed dimensional embedding.
Ranked #1 on Multimodal Emotion Recognition on MELD
Emotion Classification Emotion Recognition in Conversation +1
no code implementations • 1 Mar 2023 • Shikha Baghel, Shreyas Ramoji, Sidharth, Ranjana H, Prachi Singh, Somil Jain, Pratik Roy Chowdhuri, Kaustubh Kulkarni, Swapnil Padhi, Deepu Vijayasenan, Sriram Ganapathy
The challenge attempts to highlight outstanding issues in speaker diarization (SD) in multilingual settings with code-mixing.
no code implementations • 24 Feb 2023 • Prachi Singh, Amrit Kaul, Sriram Ganapathy
We also propose an approach to jointly update the embedding extractor and the GNN model to perform end-to-end speaker diarization (E2E-SHARC).
no code implementations • 26 Aug 2022 • Shrutina Agarwal, Sriram Ganapathy, Naoya Takahashi
In this paper, we propose a model to perform style transfer of speech to singing voice.
1 code implementation • 27 Jun 2022 • Debottam Dutta, Debarpan Bhattacharya, Sriram Ganapathy, Amir H. Poorjam, Deepak Mittal, Maneesh Singh
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.
no code implementations • 11 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.
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.
no code implementations • 4 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.
1 code implementation • 14 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.
no code implementations • 12 Aug 2021 • Anurenjan Purushothaman, Anirudh Sreeram, Rohit Kumar, Sriram Ganapathy
The dereverberated envelopes are used for feature extraction in speech recognition.
1 code implementation • 9 Aug 2021 • Rohit Kumar, Anurenjan Purushothaman, Anirudh Sreeram, Sriram Ganapathy
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
no code implementations • 30 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.
no code implementations • 24 Jun 2021 • R G Prithvi Raj, Rohit Kumar, M K Jayesh, Anurenjan Purushothaman, Sriram Ganapathy, M A Basha Shaik
This paper presents the details of the SRIB-LEAP submission to the ConferencingSpeech challenge 2021.
no code implementations • 21 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.
1 code implementation • 1 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.
no code implementations • 18 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.
1 code implementation • 19 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.
no code implementations • 6 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.
no code implementations • 5 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 Automatic Speech Recognition (ASR) +3
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.
no code implementations • 11 Mar 2021 • Jaswanth Reddy Katthi, Sriram Ganapathy
The experiments are performed on EEG data collected from subjects listening to natural speech and music.
1 code implementation • 17 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.
3 code implementations • 2 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.
1 code implementation • 11 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.
1 code implementation • 10 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
no code implementations • 7 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 Automatic Speech Recognition (ASR) +2
1 code implementation • 12 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.
no code implementations • 2 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.
1 code implementation • 10 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.
no code implementations • 7 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.
1 code implementation • 20 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.
1 code implementation • 29 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 Automatic Speech Recognition (ASR) +3
no code implementations • 28 Nov 2019 • Rohit Kumar, Anirudh Sreeram, Anurenjan Purushothaman, Sriram Ganapathy
These models are trained using a paired corpus of clean and noisy recordings (teacher model).
no code implementations • 13 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 Automatic Speech Recognition (ASR) +1
1 code implementation • 18 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.
no code implementations • 25 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.
no code implementations • 5 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 Automatic Speech Recognition (ASR) +2
no code implementations • 23 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.