Achintya kumar Sarkar

About

Achintya Kr. Sarkar received the B.Tech. in Electronics & Instrumentation Engineering, M. Tech. (with gold medal) in Instrumentation and Control Engineering degrees from the University of Kalyani, India, in 2003, and Punjab Technical University, India, in 2006, respectively, and a Ph.D. degree from IIT Madras, Chennai, India, in 2011. From 2011 to 2017, he was working as a postdoctoral research fellow in several laboratories in abroad: Laboratoire Informatique d’Avignon, France, LIMSI-CNRS, Paris, France, Department of Electrical and Electronic Engineering, University College Cork, Ireland, and the Department of Electronic Systems, Aalborg University, Denmark. During the postdoc, he was working on the various prestigious research and innovation projects: European Horizon 2020 (OCTAVE) – developed scalable trusted biometric authentication service using voice, European research and development program (Quaero) and Neoprism (Ireland) – pattern recognition for continuous neurological monitoring in Neonates. From 2018 -May, 2020, he was working as a professor in the School of Electronics Engineering, VIT-AP University, India.
His research interests include speech signal processing, biomedical signal processing, speaker recognition, spoofing countermeasure, seizure detection and application of machine learning in the above areas.

Employment

• Aalborg University Post-doc
  2016 - 2017
• University College Cork Senior Postdoc
  2014 - 2015
• Laboratoire d'Informatique pour la Mécanique et les Sciences de l'Ingénieur Postdoc
  2012 - 2014
• University of Avignon Postdoc
  2011 - 2012

Education

• Indian Institute of Technology Madras PhD
  2006 - 2011

Projects & Funding

Projects & funding information is unavailable.

Publications (18)

• Demonstration of CuO/n-Si-Based ISCM Devices Using Broadband Spectrum Measurements
  IEEE Transactions on Instrumentation and Measurement 2026 DOI: 10.1109/TIM.2026.3709456
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• Study of Various End-to-End Keyword Spotting Systems on the Bengali Language Under Low-Resource Condition
  2023 DOI: 10.1007/978-3-031-48312-7_9
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• Self-segmentation of pass-phrase utterances for deep feature learning in text-dependent speaker verification
  Computer Speech & Language 2021 DOI: 10.1016/j.csl.2021.101229
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• Vocal Tract Length Perturbation for Text-Dependent Speaker Verification With Autoregressive Prediction Coding
  IEEE Signal Processing Letters 2021 DOI: 10.1109/LSP.2021.3055180
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• rVAD: An unsupervised segment-based robust voice activity detection method
  Computer Speech & Language 2020 DOI: 10.1016/j.csl.2019.06.005
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• Time-Contrastive Learning Based Deep Bottleneck Features for Text-Dependent Speaker Verification
  IEEE/ACM Transactions on Audio, Speech, and Language Processing 2019 DOI: 10.1109/taslp.2019.2915322
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• Incorporating pass-phrase dependent background models for text-dependent speaker verification
  Computer Speech and Language 2018 DOI: 10.1016/j.csl.2017.07.010
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• The I4U mega fusion and collaboration for NIST speaker recognition evaluation 2016
  Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH 2017 DOI: 10.21437/Interspeech.2017-203
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• Toward a Personalized Real-Time Diagnosis in Neonatal Seizure Detection
  IEEE Journal of Translational Engineering in Health and Medicine 2017 DOI: 10.1109/jtehm.2017.2737992
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• RedDots replayed: A new replay spoofing attack corpus for text-dependent speaker verification research
  ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings 2017 DOI: 10.1109/ICASSP.2017.7953187
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