most citedPairwise Discriminative Neural PLDA for Speaker Verification

4 citations · 4 across the 1 of their papers we have counts for

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5 papers

eess.AS2020

Neural PLDA Modeling for End-to-End Speaker Verification

Shreyas Ramoji, Prashant Krishnan, Sriram Ganapathy

While deep learning models have made significant advances in supervised classification problems, the application of these models for out-of-set verification tasks like speaker reco…

eess.AS2020

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

Shareef Babu Kalluri, Deepu Vijayasenan, Sriram Ganapathy +2

Many commercial and forensic applications of speech demand the extraction of information about the speaker characteristics, which falls into the broad category of speaker profiling…

eess.AS20204 cited

Pairwise Discriminative Neural PLDA for Speaker Verification

Shreyas Ramoji, Prashant Krishnan, Prachi Singh +1

The state-of-art approach to speaker verification involves the extraction of discriminative embeddings like x-vectors followed by a generative model back-end using a probabilistic…

eess.AS2020

NPLDA: A Deep Neural PLDA Model for Speaker Verification

Shreyas Ramoji, Prashant Krishnan, Sriram Ganapathy

The state-of-art approach for speaker verification consists of a neural network based embedding extractor along with a backend generative model such as the Probabilistic Linear Dis…

eess.AS2020

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

Shreyas Ramoji, Prashant Krishnan, Bhargavram Mysore +2

The NIST Speaker Recognition Evaluation - Conversational Telephone Speech (CTS) challenge 2019 was an open evaluation for the task of speaker verification in challenging conditions…