Journal papers (Peer-review):

1. B. Sharmila, A. K. Sarkar and P. Dwivedi, "Self-Powered, Ultra-Fast CuO Nanocubes Surface for Broadband Photosensing Applications," in IEEE Sensors Journal, 2024,  doi: 10.1109/JSEN.2024.3416214 

2. J. Yedukondalu,  S. H. Karaddi, C. H. H. Bindu,  D. Sharma, A. K. Sarkar, L. D. Sharma. Automated Metal Surface Flaws Detection Using Convolutional Neural Network and Deep Visualization Analysis. Arabian Journal for Science and Engineering, 2024, 10.1007/s13369-024-09230-z. 

3. A.K. Sarkar, Z.-H. Tan,  On Training Targets and Activation Functions for Deep Representation Learning in Text-Dependent Speaker Verification. Acoustics 2023, 5, 693-713. https://doi.org/10.3390/acoustics5030042 

4. Priyanka Singh, Samir Kumar Borgohain, Achintya Kumar Sarkar, Jayendra Kumar,  Lakhan Dev Sharma.. Feed-Forward Deep Neural Network (FFDNN)-Based Deep Features for Static Malware Detection. International Journal of Intelligent Systems, vol. 2023, 2023. https://doi.org/10.1155/2023/9544481. 

5. A. K. Sarkar, Z.-H. Tan. Self-Segmentation of Pass-Phrase Utterances for Deep Feature Learning in Text-Dependent Speaker Verification. Computer Speech & Language, volume 70, November 2021. https://doi.org/10.1016/j.csl.2021.101229

6.  A. K. Sarkar, Z.-H. Tan. Vocal Tract Length Perturbation for Text-Dependent Speaker Verification with Autoregressive Prediction Coding.  IEEE Signal Processing Letters, vol. 28, pp. 364-368, 2021. doi: 10.1109/LSP.2021.3055180.

7. P. Dwivedi , A. K. Sarkar , C. Chakraborty , M. Singha , V. Rojwal.  Application of Artificial Intelligence on Post Pandemic Situation and Lesson Learn for Future Prospects. Journal of Experimental & Theoretical Artificial Intelligence, 35:3, 327-344, 2021. https://doi.org/10.1080/0952813X.2021.1958063

8. Z.-H. Tan, A. K. Sarkar, N. Dehak. rVAD: An Unsupervised Segment-Based Robust Voice Activity Detection Method. Computer speech & Language: Volume 59, January 2020, pp. 1-21.  (ISCA Best Research Paper Award 2023.)

9. A. K. Sarkar, Z.-H. Tan, H. Tang, S. Shon, J. Glass. Time-Contrastive Learning Based Deep Bottleneck Features for Text-Dependent Speaker Verification. IEEE/ACM Transactions on Audio, Speech and Language, Processing: Volume: 27, August 2019, pp. 1267 -1279.

10. A. K. Sarkar, Z.-H. Tan. Incorporating pass-phrase dependent background models for text-dependent speaker verification. Computer speech & Language: Volume 47, January 2018, pp. 259-271. 

11. A. Temko, A. K. Sarkar, G. Boylan, S. Mathieson, W. Marnane, G. Lightbody. Towards a Personalized Real-Time Diagnosis in Neonatal Seizure Detection. IEEE Journal of Translational Engineering in Health and Medicine, Volume: 5, September 2017. DOI:10.1109/JTEHM.2017.2737992. 

12. A. K. Sarkar, J.F Bonastre, D. Matrouf. A Study on the Roles of Total Variability Space and Session Variability Modeling in Speaker Recognition. International Journal of Speech Technology: Volume 19, Issue 1 (2016), pp. 111-120.

13.  A. K. Sarkar, J. F. Bonastre. Sub-vector based biometric speaker verification using MLLR super-vector. International Journal of Speech  Technology: Volume 19, Issue 1 (2016), pp.41-54.

14. A. K. Sarkar, C. T. Do, V. B. Le, C. Barras. Combination of Cepstral and Phonetically Discriminative Features for Speaker Verification. IEEE Signal Processing Letters, Volume: 21, Issue: 9, Sept. 2014, pp. 1040-1044. 

15. A. K. Sarkar, S Umesh. Multiple Background Models for Speaker Verification Using the Concept of Vocal Tract Length and MLLR Super-vector. International Journal of Speech Technology: Volume 15, Issue 3 (2012), pp. 351-364.

Conference papers (Peer-review):

1. Achintya Kr. Sarkar, Tulika Basu, Rajib Roy, Joyanta Basu, Michael Tongbram, Yamben Jina Chanu and Priyanka Dwivedi. “Study of Various End-to-End Keyword Spotting Systems on the Bengali language under Low-resource  Condition”,  SPECOM 2023, LNAI, vol. 14339, India.

2. M. Sahidullah, A. K. Sarkar, V. Vestman, X. Liu,  R. Serizel, T. Kinnunen, Z.-H. Tan, E. Vincent. UIAI System for Short-Duration Speaker Verification Challenge 2020.  In proc. of  IEEE Spoken Language Technology (SLT) Workshop 2021.

3. A. K. Sarkar, H. Sarma, P. Dwivedi, Z.-H. Tan.  Data augmentation enhanced speaker enrollment for text-dependent speaker verification. In proc. of  IEEE ICEPE 2020.

4. A. K. Sarkar, M. Sahidullah, Z.-H. Tan, T. Kinnunen. Improving Speaker Verification Performance in Presence of Spoofing Attacks Using Out-of-Domain Spoofed Data. In proc. of INTERSPEECH 2017, pp. 2611-2615.      

5. A. K. Sarkar, Z.-H. Tan. Time-Contrastive Learning Based DNN Bottleneck Features for Text Dependent Speaker Verification. NIPS Time Series Workshop 2017, Long Beach, CA, USA.

6. T. Kinnunen, M. Sahidullah, M. Falcone, L. Costantini, R. G. Hautamaki, D. Thomsen, A. Sarkar, Z.-H. Tan, H. Delgado, M. Todisco, N. Evans, V. Hautamaki, K. A. Lee. RedDots Replayed: A New Replay Spoofing Attack Corpus for Text-Dependent Speaker Verification Research. In Proc. of IEEE Int. Conf. Acoust. Speech Signal Processing (ICASSP), 2017, pp. 5395-5399.

7. K. A. Lee and et al. The I4U Mega Fusion and Collaboration for NIST Speaker Recognition Evaluation 2016. In proc. of INTERSPEECH 2017, pp. 1328-1332.

8. A. K. Sarkar, Z.-H. Tan. Text Dependent Speaker Verification Using Un-supervised HMM-UBM and Temporal GMM-UBM. In Proc. of INTERSPEECH, 2016, pp. 425-429, USA.

9. H. Yu, A. Sarkar, D. A. L. Thomsen, Z.-H. Tan, Z. Ma, J. Guo. Effect of Multi-condition Training and Speech Enhancement Methods on Spoofing Detection. In Proc. of International Workshop on Sensing, Processing and Learning for Intelligent Machines (SPLINE), 2016, pp. 1-5, Denmark.

10. H. Delgado, M. Todisco, M. Sahidullah, A. K. Sarkar, N. Evans, T. Kinnunen, Z.-H. Tan. Further Optimizations of Constant Q cepstral Processing for Integrated Utterance verification and Text-dependent speaker verification. In Proc. of IEEE Spoken Language Technology (SLT) Workshop, 2016, pp. 179-185.

11. T. Kinnunen, M. Sahidullah, I. Kukanov, H. Delgado, M. Todisco, A. Sarkar, N.B. Thomsen, N. Evans, Z.-H. Tan. Utterance Verification for Text-Dependent Speaker Recognition: a Comparative Assessment Using the RedDots Corpus. In Proc. of INTERSPEECH, 2016, pp. 430-434, USA.

12. A. Temko, A. K. Sarkar, G. Lightbody. Detection of Seizures in Intracranial EEG: UPenn and Mayo Clinics Seizure Detection Challenge. In Proc. of IEEE Engineering in Medicine and Biology Society (EMBC), 2015, pp. 6582-6585, Italy.

13. H. Bredin, A. Laurent, A. K. Sarkar, V. B. Le, S. Rosset, C. Barras. Person Instance Graphs for Named Speaker Identification in TV Broadcast. In Proc. of Speaker and Language Recognition Workshop- Odyssey, 2014, pp. 179-186, Finland.

14. A. K. Sarkar, C. Barras. Anchor and UBM-based Multi-Class MLLR M-Vector System for Speaker Verification. In Proc. of INTERSPEECH, 2013, pp. 2450-2454, France.

15. H. Bredin and et al. Qcompere @ Repere 2013. In Proc. of First Workshop on Speech, Language and Audio in Multimedia (SLAM), 2013, pp. 49-54, France.

16. A. K. Sarkar, C. Barras. Multi-Class UBM-Based MLLR m-Vector System for Speaker Verification. In Proc. of European Signal Processing Conference (EUSIPCO), 2013, pp.1-5, Morocco.

17. A. K. Sarkar, C. Barras, V. B. Le. Lattice MLLR based m-vector System for Speaker Verification. In Proc. of IEEE Int. Conf. Acoust. Speech Signal Processing (ICASSP), 2013, pp. 7654-7658, Canada

18. A. K. Sarkar, D. Matrouf, P. M. Bousquet, J. F. Bonastre. Study of the Effect of I-vector Modeling on Short and Mismatch Utterance Duration for Speaker Verification. In Proc. of INTERSPEECH, 2012, pp. 2662-2665, USA.

19. A. K. Sarkar, J. F. Bonastre, D. Matrouf . Speaker Verification using m-vector Extracted from MLLR Super-Vector. In Proc. of 20th European Signal Processing Conference (EUSIPCO), 2012, pp. 21-25, Romania.

20. A. K. Sarkar, S. Umesh, J. F. Bonastre. Computationally Efficient Speaker Identification Using Fast-MLLR Based Anchor Modeling. In Proc. of IEEE Int. Conf. Acoust. Speech Signal Processing (ICASSP), 2012, pp. 4357-4360, Japan.

21. A. K. Sarkar, S. Umesh. Eigen-voice Based Anchor Modeling System for Speaker Identification using MLLR Super-vector. In Proc. of INTERSPEECH, 2011, pp. 2357-2360, Italy.

22. A. K. Sarkar, S. Umesh. Use of VTL-wise Models in Feature-Mapping Framework to Achieve Performance of Multiple-Background Models in Speaker Verification. In Proc. of IEEE Int. Conf. Acoust. Speech Signal Processing (ICASSP), 2011, pp. 4552 - 4555, Czech Republic.

23. A. K. Sarkar, S. Umesh. Fast Computation of Speaker Characterization Vector using MLLR and Sufficient Statistics in Anchor Model Framework. In Proc. of INTERSPEECH, 2010, pp. 2738-2741, Japan.

24. A. K. Sarkar, S. Umesh, S. P. Rath. Computationally Efficient Speaker Identification for Large Population Tasks using MLLR and Sufficient Statistics. In Proc. of Speaker and Language Recognition Workshop- Odyssey 2010, pp. 7-11, Czech Republic.

25. A. K. Sarkar, S. Umesh. Investigation of Speaker-Clustered UBMs based on Vocal Tract Lengths and MLLR matrices for Speaker Verification. In Proc. of Speaker and Language Recognition Workshop- Odyssey 2010, pp. 286-293, Czech Republic.

26. A. K. Sarkar, S. P. Rath, S. Umesh. Fast Approach to Speaker Identification for Large Population Using MLLR and Sufficient Statistics. In Proc. of National Conference on Communications (NCC), 2010, India.

27. A. K. Sarkar, S. P. Rath, S. Umesh. Vocal Tract Length Normalization Factor Based Speaker-Cluster UBM for Speaker Verification. In Proc. of National Conference on Communications (NCC), 2010, India.

28. S. P. Rath, A. K. Sarkar, S Umesh. Effect of Jacobian Compensation in Linear Transformation Based VTLN under Matched and Mismatched Speaker Conditions. In Proc. of National Conference on Communications (NCC), 2010, India.

29. A. K. Sarkar, S. Umesh, S. P. Rath. Text-Independent Speaker Identification Using Vocal Tract Length Normalization for Building Universal Background Model. In Proc. of INTERSPEECH, 2009, pp. 2331-2334, UK.

30. S. P. Rath, S. Umesh, A. K. Sarkar. Using VTLN Matrices for Rapid and Computationally Efficient Speaker Adaptation with Robustness to First-Pass Transcription Errors. In Proc. of INTERSPEECH, 2009, pp. 572-575, UK.