NPLDA: A deep neural PLDA model for speaker verification
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 …
embedding extractor along with a backend generative model such as the Probabilistic …
Ssnet: A deep learning approach for protein-ligand interaction prediction
Computational prediction of Protein-Ligand Interaction (PLI) is an important step in the
modern drug discovery pipeline as it mitigates the cost, time, and resources required to …
modern drug discovery pipeline as it mitigates the cost, time, and resources required to …
EfficientTDNN: Efficient architecture search for speaker recognition
Convolutional neural networks (CNNs), such as the time-delay neural network (TDNN), have
shown their remarkable capability in learning speaker embedding. However, they …
shown their remarkable capability in learning speaker embedding. However, they …
PLDA inspired Siamese networks for speaker verification
The deep learning methodologies in state-of-the-art speaker recognition systems are
predominantly limited to the extraction of recording level embeddings. This is usually …
predominantly limited to the extraction of recording level embeddings. This is usually …
Multi-stream convolutional neural network with frequency selection for robust speaker verification
W Yao, S Chen, J Cui, Y Lou - arXiv preprint arXiv:2012.11159, 2020 - arxiv.org
Speaker verification aims to verify whether an input speech corresponds to the claimed
speaker, and conventionally, this kind of system is deployed based on single-stream …
speaker, and conventionally, this kind of system is deployed based on single-stream …
LEAP System for SRE19 CTS Challenge--Improvements and Error Analysis
The NIST Speaker Recognition Evaluation-Conversational Telephone Speech (CTS)
challenge 2019 was an open evaluation for the task of speaker verification in challenging …
challenge 2019 was an open evaluation for the task of speaker verification in challenging …
[PDF][PDF] Supervised Learning Approaches for Language and Speaker Recognition
S Ramoji - 2023 - leap.ee.iisc.ac.in
In the age of artificial intelligence, it is important for machines to figure out who is speaking
automatically and in what language-a task humans are naturally capable of. Developing …
automatically and in what language-a task humans are naturally capable of. Developing …
Collaborative Filtering based Generative Networks
R Srinivas - 2021 - scholar.smu.edu
Collaborative Filtering, a popular method for recommendation engines, models its
predictions using past interactions between the entities in question (aka users/movies or …
predictions using past interactions between the entities in question (aka users/movies or …
Multi-Stream Convolutional Neural Network with Frequency Selection for Robust Speaker Verification
W Yao, S Chen, J Cui, Y Lou - Computing and Informatics, 2024 - cai.sk
Speaker verification aims to verify whether an input speech corresponds to the claimed
speaker, and conventionally, this kind of system is deployed based on single-stream …
speaker, and conventionally, this kind of system is deployed based on single-stream …
IITG-Indigo Submissions for NIST 2018 Speaker Recognition Evaluation and Post-Challenge Improvements
This paper describes the submissions of team Indigo at Indian Institute of Technology
Guwahati (IITG) to the NIST 2018 Speaker Recognition Evaluation (SRE18) challenge …
Guwahati (IITG) to the NIST 2018 Speaker Recognition Evaluation (SRE18) challenge …