Automatic Speaker Recognition: Current Approaches and Progress in Last Six Decades
7. Conclusion
50
Automatic Speaker Recognition: Current Approaches and Progress in Last Six Decades
Vol 9 | Issue 3 | July-Sept 2017 | www.informaticsjournals.com/index.php/gjeis GJEIS | Print ISSN: 0975-153X | Online ISSN: 0975-1432
including access control, voice authentication, banking by tel- ephone and many more5,17. It is very difficult to find the fix voice parameters by which a good speaker recognition system with maximum accuracy can be developed. Therefore to design and develop robust speaker recognition system, continuous effort is needed. Speaker recognition technology has many advance- ment and development till date but technology development and evaluation are two sides of the same coin. So keeping this point in mind it can be concluded that without having a good measure of progress nobody can make valuable progress5. Till date various investigations have been proposed for evaluation of speaker rec- ognition but in real a complete tool has not yet been developed.
5. Factors Affecting the
Nilu Singh, Alka Agrawal and R. A. Khan Theme Based Paper
on Speech and Audio Processing. 1995; 3(1):72–83. https://doi.
org/10.1109/89.365379
3. Furui S. An Overview of Speaker Recognition Technology. ESCA Workshop on Automatic Speaker Recognition, Identification and Verification, Martigny, Switzerland. 1994 Apr; 1–9.
4. Melin H. Speaker Verification in Telecommunication. Department of Speech,
5. Furui S. Speech and Speaker Recognition Evaluation. Evaluation of Text and Speech Systems. Springer. 2007; 1–27. https://doi.
org/10.1007/978-1-4020-5817-2_1
6. Speert D. Brain Facts A Primer on the Brain and Nervous System.
Society for Neuroscience, United States of America, Sixth Edition.
2006; 1–80.
7. Campbell JP Jr, Speaker Recognition, Department of Defense Fort Meade, MD, pp 1–26
8. Soong FK et al. A Vector Quantization Approach to Speaker Recognition. AT&T
9. Atal BS. Effectiveness of Linear Prediction Characteristics of the Speech Wave for Automatic Speaker Identification and verification.
Journal Acoustical Society of America. 1974 Jun; 55(6):1304–12.
https://doi.org/10.1121/1.1914702 PMid:4846727
10. Aldhaheri RW, Al-Saadi FE. Robust Text-independent Speaker Recognition with Short Utterance in Noisy Environment Using SVD as a Matching Measure. J. King Saud Univ, Comp and Info Sci. 2004;
17:23–41.
11. Chougule SV, Chavan MS. Robust Spectral Features for Automatic Speaker Recognition in Mismatch Condition. Procedia Computer Science. 2015; 58:272–9. https://doi.org/10.1016/j.procs.2015.08.021 12. Shao Y, Wang DL. Robust Speaker Identification Using Auditory
Features and Computational Auditory Scene Analysis. Department of Computer Science and Engineering, Center for Cognitive Science, The Ohio State University, Columbus, ICASSP, 1-4244-1484-9/08, IEEE. 2008; 1589–92.
13. Bimbot F, Mathan L. Second- Order Statistical Measures for Text- Independent Speaker Identification. ESCA Workshop on Automatic Speaker Recognition, Identification and Verification, Martigny, Switzerland. 1994 Apr; 51–4.
14. Aldhaheri RW, Al-Saadi FE. Text-Independent Speaker Identification in Noisy Environment Using Singular Value Decomposition. ICICS- PCM, Singapore, IEEE. 2003 Dec; 1–5.
15. Krishnamoorthy P et al. Speaker recognition under limited data con- dition by noise addition. Expert Systems with Applications, Elsevier.
2011; 38:13487– https://doi.org/10.1016/j.eswa.2011.04.069
16. Ganapathy S et al. Robust Feature Extraction using Modulation Filtering of Autoregressive Models, IEEE/ACM Transactions on Audio, Speech, and Language Processing. 2014 Aug; 22(8):1285–95.
17. Singh N, Khan RA, Shree R. Applications of Speaker Recognition, International Conference on Modeling, Optimization and Computing (ICMOC–2012). Procedia Engineering, Elsevier. 2012;
38:3122–6. https://doi.org/10.1016/j.proeng.2012.06.363
18. Reynolds DA. Automatic Speaker Recognition: Current Approaches and Future Trends. MIT Lincoln Laboratory, Lexington, MA USA.
2001; 1–6.
19. Hansen JHL, Hasan T. Speaker Recognition by Machines and Humans. IEEE Signal Processing Magazine. 2015 Nov; 74:74–99.
https://doi.org/10.1109/MSP.2015.2462851
20. Singh N, Agrawal A, Khan RA. A Critical Review on Automatic Speaker Recognition. Science Journal of Circuits, Systems and Signal Processing. 2015 Jul; 4(2):14–7.
21. Meuwly D. Forensic Individualisation from Biometric Data. Science and Justic . 2006; 46(4):205–13. https://doi.org/10.1016/S1355- 0306(06)71600-8
22. Bishop J. Using the concepts of forensic linguistics, bleasure and motif to enhance multimedia forensic evidence collection. The 2014 International Conference on Security and Management SAM”14, Monte Carlo Resort in Las Vegas, Nevada USA. 2014. p. 21–4.
23. Tolba H. A high-performance text-independent speaker identifica- tion of Arabic speakers using a CHMM-based approach. Alexandria Engineering Journal. 2011; 50:43–7 https://doi.org/10.1016/j.
aej.2011.01.007
24. Maesa A et al. Text Independent Automatic Speaker Recognition System Using Mel-Frequency Cepstrum Coefficient and Gaussian Mixture Models. Journal of Information Security. 2012; 3:335–40.
https://doi.org/10.4236/jis.2012.34041
25. Chou C-H et al. A Statistical Out-of-Speaker Detection Approach for Smart Home Voice-Control Scenario of Protective Warming Care on FPGA, ASE BD & SI, Kaohsiung, Taiwan. 2015 Oct; 1–4.
26. Perrin E et al. Phonatory Signature of the Deaf Child”, ESCA Workshop on Automatic Speaker Recognition, Identification and Verification, Martigny, Switzerland. 1994 Apr; 201–4.
27. Yucesoy E et al. A new approach with score-level fusion for the clas- sification of a speaker age and gender. Computers and Electrical Engineering (Elsevier). 2016; 53:29–39 https://doi.org/10.1016/j.
compeleceng.2016.06.002
28. Shen X et al. A Speaker Recognition Algorithm Based on Factor Analysis. https://doi.org/10.1109/CISP.2014.7003905
29. Anzar SM et al. Efficient online and offline template update mechanisms for speaker recognition. Computers and Electrical Engineering (Elsevier). 2016; 50:10–25. https://doi.org/10.1016/j.
compeleceng.2015.12.003
30. Sanchez-Cortina I et al. Speaker-adapted confidence measures for speech recognition of video lectures. Computer Speech and Language (Elsevier). 2016; 37:11–23. https://doi.org/10.1016/j.csl.2015.10.003 31. Yegna Narayana B, Mahadeva Prasanna SR, Zachariah JM, Gupta
Prasanna CS. Combining Evidence from Source, Suprasegmental and Spectral Features for a Fixed-Text Speaker Verification System. IEEE Transactions on Speech and Audio Processing. 2005; 13(4):575–82.
https://doi.org/10.1109/TSA.2005.848892
32. Nakasone H. Voice Recognition Capabilities at the FBI. Insttute for Defense and Government Advancement. 2014.
33. Zhang X et al. Rapid Speaker Adaptation in Latent Speaker Space Withnon- Negative Matrix Factorization. Elsevier, Science Direct, Speech Communication. 2013; 55:893–908. https://doi.org/10.1016/j.
specom.2013.05.001
34. Colombi J, Ruck D, Rogers S, Oxley M, Anderson T. Cohort Selection and Word Grammer Effects for Speaker Recognition. In International
52
Automatic Speaker Recognition: Current Approaches and Progress in Last Six Decades
Vol 9 | Issue 3 | July-Sept 2017 | www.informaticsjournals.com/index.php/gjeis GJEIS | Print ISSN: 0975-153X | Online ISSN: 0975-1432 Conference on Acoustics, Speech, and Signal Processing in Atlanta,
IEEE. 1996; 85–8.
35. Dehak N et al. Modeling Prosodic Features With Joint Factor Analysis for Speaker Verification. IEEE Transactions on Audio, Speech, And Language Processing. 2007 Sep; 15(7):2095–103 https://
doi.org/10.1109/TASL.2007.902758
Annexure-I
Citation:
Nilu Singh, Alka Agrawal and R. A. Khan
“Automatic Speaker Recognition: Current Approaches and Progress in Last Six Decades”, Global Journal of Enterprise Information System. Volume-9, Issue-3, July-September, 2017. (http://informaticsjournals.com/index.php/gjeis) DOI: 10.18311/gjeis/2017/15973 Conflict of Interest:
Author of a Paper had no conflict neither financially nor academically.
Source: http://www.ithenticate.com/