Zobrazeno 1 - 10 of 37 pro vyhledávání: '"Achintya Kumar Sarkar"'
1
Akademický článek
On Training Targets and Activation Functions for Deep Representation Learning in Text-Dependent Speaker Verification
Autor: Achintya Kumar Sarkar, Zheng-Hua Tan
Publikováno v: Acoustics, Vol 5, Iss 3, Pp 693-713 (2023)
Deep representation learning has gained significant momentum in advancing text-dependent speaker verification (TD-SV) systems. When designing deep neural networks (DNN) for extracting bottleneck (BN) features, the key considerations include training
Externí odkaz: https://doaj.org/article/8b6a8ea0bd814ea8a727fdc561c02042
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2
Akademický článek
A systematic review of EEG based automated schizophrenia classification through machine learning and deep learning
Autor: Jagdeep Rahul, Diksha Sharma, Lakhan Dev Sharma, Umakanta Nanda, Achintya Kumar Sarkar
Publikováno v: Frontiers in Human Neuroscience, Vol 18 (2024)
The electroencephalogram (EEG) serves as an essential tool in exploring brain activity and holds particular importance in the field of mental health research. This review paper examines the application of artificial intelligence (AI), encompassing ma
Externí odkaz: https://doaj.org/article/e167ada509984ee7b5ba42e2fc510ce6
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3
Feed-Forward Deep Neural Network (FFDNN)-Based Deep Features for Static Malware Detection
Autor: Priyanka Singh, Samir Kumar Borgohain, Achintya Kumar Sarkar, Jayendra Kumar, Lakhan Dev Sharma
Publikováno v: International Journal of Intelligent Systems.
The portable executable header (PEH) information is commonly used as a feature for malware detection systems to train and validate machine learning (ML) or deep learning (DL) classifiers. We propose to extract the deep features from the PEH informati
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c664e2c6bf1c1f8840c6025bb75b8e9c
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4
Application of Artificial Intelligence on Post Pandemic Situation and Lesson Learn for Future Prospects
Autor: Vineet Rojwal, Achintya Kumar Sarkar, Monoj Kumar Singha, Priyanka Dwivedi, Chinmay Chakraborty
Publikováno v: Journal of Experimental & Theoretical Artificial Intelligence. 35:327-344
Coronavirus disease (COVID-19) pandemic has intensively damaged human socio-economic lives and the growth of countries around the world. Many efforts have been made in the direction of artificial i...
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::d2059c3928120982066f9be0aefda95a
https://doi.org/10.1080/0952813x.2021.1958063
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6
Vocal Tract Length Perturbation for Text-Dependent Speaker Verification with Autoregressive Prediction Coding
Autor: Achintya Kumar Sarkar, Zheng-Hua Tan
Publikováno v: Sarkar, A & Tan, Z-H 2021, ' Vocal Tract Length Perturbation for Text-Dependent Speaker Verification with Autoregressive Prediction Coding ', I E E E Signal Processing Letters, vol. 28, 9339931, pp. 364-368 . https://doi.org/10.1109/LSP.2021.3055180
In this letter, we propose a vocal tract length (VTL) perturbation method for text-dependent speaker verification (TD-SV), in which a set of TD-SV systems are trained, one for each VTL factor, and score-level fusion is applied to make a final decisio
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a0ee7bc92db7ce3102ba8c274e9d3e13
http://arxiv.org/abs/2011.12536
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7
Data augmentation enhanced speaker enrollment for text-dependent speaker verification
Autor: Himangshu Sarma, Priyanka Dwivedi, Zheng-Hua Tan, Achintya Kumar Sarkar
Publikováno v: Sarkar, A K, Sarma, H, Dwivedi, P & Tan, Z-H 2021, Data Augmentation Enhanced Speaker Enrollment for Text-Dependent Speaker Verification . in 3rd International Conference on Energy, Power and Environment : Towards Clean Energy Technologies, ICEPE 2020 ., 9404373, IEEE, 2020 3rd International Conference on Energy, Power and Environment: Towards Clean Energy Technologies, Shillong, India, 05/03/2021 . https://doi.org/10.1109/ICEPE50861.2021.9404373
Data augmentation is commonly used for generating additional data from the available training data to achieve a robust estimation of the parameters of complex models like the one for speaker verification (SV), especially for under-resourced applicati
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9612280168879b74fc3e60fc1f476f39
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8
UIAI System for Short-Duration Speaker Verification Challenge 2020
Autor: Achintya Kumar Sarkar, Romain Serizel, Zheng-Hua Tan, Ville Vestman, Emmanuel Vincent, Sahidullah, Xuechen Liu, Tomi Kinnunen
Publikováno v: IEEE Spoken Language Technology Workshop 2021
IEEE Spoken Language Technology Workshop 2021, IEEE, Jan 2021, Shenzhen, China
SLT 2021-IEEE Spoken Language Technology Workshop
SLT 2021-IEEE Spoken Language Technology Workshop, IEEE, Jan 2021, Shenzhen / Virtual, China. ⟨10.1109/SLT48900.2021.9383596⟩
Sahidullah, M, Sarkar, A K, Vestman, V, Liu, X, Serizel, R, Kinnunen, T, Tan, Z-H & Vincent, E 2021, UIAI System for Short-Duration Speaker Verification Challenge 2020 . in 2021 IEEE Spoken Language Technology Workshop (SLT) ., 9383596, IEEE, pp. 323-329, 2021 IEEE Spoken Language Technology Workshop (SLT), Shenzhen, China, 19/01/2021 . https://doi.org/10.1109/SLT48900.2021.9383596
SLT
International audience; In this work, we present the system description of the UIAI entry for the short-duration speaker verification (SdSV) challenge 2020. Our focus is on Task 1 dedicated to text-dependent speaker verification. We investigate diffe
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::01641793fd276b17ef80cd3ecd287002
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9
Self-segmentation of pass-phrase utterances for deep feature learning in text-dependent speaker verification
Autor: Achintya Kumar Sarkar, Zheng-Hua Tan
Publikováno v: Sarkar, A K & Tan, Z-H 2021, ' Self-Segmentation of Pass-Phrase Utterances for Deep Feature Learning in Text-Dependent Speaker Verification ', Computer Speech and Language, vol. 70, 101229 . https://doi.org/10.1016/j.csl.2021.101229
In this paper, we propose a novel method to segment and label pass-phrase utterances for training deep neural network (DNN) bottleneck (BN) features for text-dependent speaker verification (TD-SV). Specifically, gender-dependent hidden Markov models
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1ce27d5516826224f355fa8be78fbb6a
https://doi.org/10.1016/j.csl.2021.101229
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10
rVAD: An Unsupervised Segment-Based Robust Voice Activity Detection Method
Autor: Achintya Kumar Sarkar, Zheng-Hua Tan, Najim Dehak
Publikováno v: Tan, Z H, Sarkar, A K & Dehak, N 2020, ' rVAD : An unsupervised segment-based robust voice activity detection method ', Computer Speech and Language, vol. 59, pp. 1-21 . https://doi.org/10.1016/j.csl.2019.06.005
This paper presents an unsupervised segment-based method for robust voice activity detection (rVAD). The method consists of two passes of denoising followed by a voice activity detection (VAD) stage. In the first pass, high-energy segments in a speec
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::41c70b11ba096f4edc3fc92510e7380b
http://arxiv.org/abs/1906.03588
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