[1. Baginski, M. Robust Speech Detection in High Levels of Background Noise. Imperial College, Department of Electrical and Electronic Engineering, Final Year Project Report, 2014, pp. 1-67.]Search in Google Scholar
[2. Beuyeuk, O., M. Arslan. Model Selection and Score Normalization for Text-Dependent Single Utterance Speaker Verification. - Turkish Journal of Electrical Engineering & Computer Sciences, Vol. 20, 2012, No Sup. 2, pp. 1277-1295.10.3906/elk-1103-35]Search in Google Scholar
[3. Bengio, S., J. Marietho z. A Statistical Significance Test for Person Authentication. - In: Proc. of ODYSSEY - The Speaker and Language Recognition Workshop, 2004, pp. 237-244.]Search in Google Scholar
[4. Burileanu, C., et al. On Performance Improvement ofa Speaker Verification System Using Vector Quantization, Cohorts and Hybrid Cohort-World Models. - International Journal of Speech Technology, Vol. 5, 2002, pp. 247-257.10.1023/A:1020244924468]Search in Google Scholar
[5. Center for Spoken Language Understanding, Speech Enhancement and Assessment Resource (Sp EAR) Database. Oregon Graduate Institute of Science and Technology, September 2016. http://www.cslu.ogi.edu/nsel/data/SpEAR_lombard.html]Search in Google Scholar
[6. Dan Ellis’s Home Page, Sound Examples for Projects. Columbia University, August 2017. https://www.ee.columbia.edu/~dpwe/]Search in Google Scholar
[7. Dietterich, T. Approximate Statistical Tests for Comparing Supervised Classification Learning Algorithms. - Neural Computation, Vol. 10, 1998, No 7, pp. 1895-1923.10.1162/0899766983000171979744903]Search in Google Scholar
[8. ETSI, Speech Processing, Transmission and Quality Aspects (STQ); Distributed Speech Recognition; Advanced Front-End Feature Extraction Algorithm; Compression Algorithms. ETSI ES 202 050 V1.1.5 (2007-01). Annex A.3. Stage 2 - VAD Logic. 2007, pp. 42-43.]Search in Google Scholar
[9. Ganchev, T. Contemporary Methods for Speech Parameterization. - Springer Briefs in Speech Technology. New York, Springer-Verlag, 2011. 114 p.10.1007/978-1-4419-8447-0]Search in Google Scholar
[10. Gales, M., S. Young. The Application of Hidden Markov Models in Speech Recognition. - Journal Foundations and Trends in Signal Processing, Vol. 1, 2008, No 3, pp. 195-304.10.1561/2000000004]Search in Google Scholar
[11. Ghaemmaghami, H., et al. Noise Robust Voice Activity Detection Using Normal Probability Testing and Time-Domain Histogram Analysis. - In: Proc. of IEEE ICASSP, 2010, pp. 4470-4473.10.1109/ICASSP.2010.5495612]Search in Google Scholar
[12. Hegde, R., H. Murthy, V. Gadde. Significance of the Modified Group Delay Feature in Speech Recognition. - IEEE Transactions on ASLP, Vol. 15, 2007, No 1, pp. 190-202.10.1109/TASL.2006.876858]Search in Google Scholar
[13. Jia, C., B. Xu. An Improved Entropy Based Endpoint Detection Algorithm. - In: Proc. of ISCSLP, 2002, pp. 96-100.]Search in Google Scholar
[14. Kitaoka, N., et al. Development of VAD Evaluation Framework CENSREC-1-Cand Investigation of Relationship between VADand Speech Recognition Performance. - In: Proc. of IEEE Workshop on Automatic Speech Recognition & Understanding (ASRU’07), 2007, pp. 607-612.]Search in Google Scholar
[15. Kyriakides, A., et al. Isolated Word Endpoint Detection Using Time-Frequency Variance Kernels. - In: Proc. of Conference Record of the 45th Asilomar Conference on Signals, Systems and Computers (ASILOMAR’11), 2011, pp. 1041-1045.10.1109/ACSSC.2011.6190069]Search in Google Scholar
[16. Luengo, I., et al. Modified LTSE-VAD Algorithm for Applications Requiring Reduced Silence Frame Misclassification. - In: Proc. of International Conference on Language Resources and Evaluation (LREC’10), 2010, pp. 1539-1544.]Search in Google Scholar
[17. Li, J., Z. Ping, J. Xinxing, D. Zhiran. Speech Endpoint Detection Method Based on TEO in Noisy Environment. - Procedia Engineering, Vol. 29, 2012, pp. 2655-2660.10.1016/j.proeng.2012.01.367]Search in Google Scholar
[18. Li, Q., J. Zheng, A. Tsai, Q. Zhou. Robust Endpoint Detection and Energy Normalization for Real-Time Speech and Speaker Recognition. - IEEE Transaction on SAP, Vol. 10, 2002, No 3, pp. 146-157.10.1109/TSA.2002.1001979]Search in Google Scholar
[19. Martin, A., et al. The DET Curve in Assessment of Detection Task Performance. - In: Proc. of EUROSPEECH, 1997, pp. 1895-1898.10.21437/Eurospeech.1997-504]Search in Google Scholar
[20. Mesa-Navarro, J., et al. An Improved Speech Endpoint Detection System in Noisy Environments by Means of Third-Order Spectra. - IEEE SP Letters, Vol. 6, 1999, No 9, pp. 224-226.10.1109/97.782065]Search in Google Scholar
[21. Myers, C., L. Rabiner, A. Rosenber g. Performance Tradeoffs in Dynamic Time Warping Algorithms for Isolated Word Recognition. - IEEE Transactions on ASSP, Vol. 28, 1980, No 6, pp. 623-635.10.1109/TASSP.1980.1163491]Search in Google Scholar
[22. Munteanu, D., S. Toma, Automatic Speaker Verification Experiments Using HMM. - In: Proc. of 8th International Conference on Communications, 2010, pp. 107-110.10.1109/ICCOMM.2010.5509021]Search in Google Scholar
[23. Ouzounov, A. BG-SRDat: A Corpus in Bulgarian Language for Speaker Recognition over Telephone Channels. - Cybernetics and Information Technologies, Vol. 3, 2003, No 2, pp. 101-108.]Search in Google Scholar
[24. Ouzounov, A. Noisy Speech Endpoint Detection Using Robust Feature. - In: Proc. of Springer International Publishing Switzerland. V. Cantoni et al., Eds. BIOMET 2014, LNCS 8897, 2014, pp. 105-117.10.1007/978-3-319-13386-7_9]Search in Google Scholar
[25. Ouzounov, A. Telephone Speech Endpoint Detection Using Mean-Delta Feature. - Cybernetics and Information Technologies, Vol. 14, 2014, No 2, pp. 127-139.10.2478/cait-2014-0025]Search in Google Scholar
[26. Parthasarathy, S., A. Rosenber g. General Phrase Speaker Verification Using Sub-Word Background Models and Likelihood-Ratio Scoring. - In: Proc. of ICSLP’96, pp. 2403-2406.]Search in Google Scholar
[27. Rabiner, L. A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition. - Proceedings of the IEEE, Vol. 77, 1989, No 2, 257-286.10.1109/5.18626]Search in Google Scholar
[28. Ramirez, J., et al. Efficient Voice Activity Detection Algorithms Using Long-Term Speech Information. - Speech Communication, Vol. 42, 2004, No 3-4, pp. 271-287.10.1016/j.specom.2003.10.002]Open DOISearch in Google Scholar
[29. Ramirez, J., et al. SVM-Based Speech Endpoint Detection Using Contextual Speech Features. - Electronics Letters, Vol. 42, 2006, No 7, pp. 426-428.10.1049/el:20064068]Open DOISearch in Google Scholar
[30. Roach, P. English Phonetics and Phonology: A Practical Course. 4th Ed. Cambridge University Press, 2009. 283 p.]Search in Google Scholar
[31. Scott, D. Scott’s Rule. - WIREs Computational Statistics, Vol. 2, 2010, pp. 497-502. 10.1002/wics.103]Open DOISearch in Google Scholar
[32. Speaker Recognition Project - Proprietary Developed Software. Internal Reports (unpublished), Fadata, Ltd., 2003.]Search in Google Scholar
[33. Tilkov, D., T. Bojadzhiev. Balgarska Fonetika (Bulgarian Phonetics), Sofia, Nauka i Izkustvo, 1977 (in Bulgarian).]Search in Google Scholar
[34. Tuononen, M., R. Hautamäki, P. Fränt i. Automatic Voice Activity Detection in Different Speech Applications. - e-Forensic, Vol. 12, 2008, pp. 1-6.10.4108/e-forensics.2008.2781]Search in Google Scholar
[35. Wu, G., et al., Fuzzy Neural Networks for Speech Endpoint Detection. - In: Proc. of 2012 International Conference on Fuzzy Theory and Its Applications, 2012, pp. 354-356.10.1109/iFUZZY.2012.6409730]Search in Google Scholar
[36. Wu, B., K. Wang. Robust Endpoint Detection Algorithm based on the Adaptive Band-Partitioning Spectral Entropy in Adverse Environments. - IEEE Transactions on SAP, Vol. 13, 2005, No 5, pp. 762-775.10.1109/TSA.2005.851909]Search in Google Scholar
[37. Yamamoto, K. et al. Robust Endpoint Detection for Speech Recognition Based on Discriminative Feature Extraction. - In: IEEE ICASSP, Vol. I, 2006, pp. 805-808.]Search in Google Scholar
[38. Yali, C. et al. A Speech Endpoint Detection Algorithm Based on Wavelet Transforms. - In: Proc. of 26th Chinese Control and Decision Conference (CCDC’14), 2014, pp. 3010-3012.10.1109/CCDC.2014.6852690]Search in Google Scholar
[39. Zhang, T., H. Huang, L. He, M. Lech. A Robust Speech Endpoint Detection Algorithm Based on Wavelet Packet and Energy Entropy. - In: Proc. of 3rd International Conference on Computer Science and Network Technology, 2013, pp. 1050-1054.10.1109/ICCSNT.2013.6967284]Search in Google Scholar
[40. Zelinski, R., F. Class. A Learning Procedure for Speaker-Dependent Word Recognition System Based on Sequential Processing of Input Tokens. - In: Proc. of IEEE ICASSP, 1983, pp. 1053-1056.]Search in Google Scholar
[41. Zhang, Z., S. Furui. Noisy Speech Recognition Based on Robust End-Point Detection and Model Adaptation. - Proceedings of IEEE ICASSP, Vol. 1, 2005, pp. 441-444.]Search in Google Scholar
[42. Zhu, J., F. Chen. The Analysis and Application ofa New Endpoint Detection Method Based on Distance of Autocorrelated Similarity. - In: Proc. of EUROSPEECH, 1999, pp. 105-108.10.21437/Eurospeech.1999-30]Search in Google Scholar