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 • 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 • 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.
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 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.