Author : Jui Trivedi 1
Date of Publication :24th January 2018
Abstract: The task of Speaker Identification (SI) technique is to determine which authorized speaker provides an utterance. Voice conversion (VC) method pacts to hide the identity of the speaker. In the Spoofing attack, a manipulated voice is employed as the system input which is voice conversion. It is obligatory to protect speech samples from Spoofing attacks like one mimicry artist can mimic the voice of any person so at that time this system fails to provide security. The security check of watermarking pattern juxtaposes speech samples from the database to identify speaker at the end of the methodology. This paper addresses the review of various techniques of Speaker identification, spoofing attack of Voice Conversion and the techniques of embedding watermarking patterns on various speech samples to avoid the false identification of a subject in Speaker Identification System.
Reference :
-
- M.A Nematollahi, S.A.R Al-Haddad, Shyamala Doraisamy and M. Ranjbari, “Digital Speech Watermarking for Anti-Spoofing Attack in Speaker Recognition”, IEEE Region 10 Symposium, 2014, pp. 476- 479.
- Ronak Bajaj, “Features based on Fourier-Bessel Expansion for Application of Speaker Identification System”, 2014
- Kinnal Dhameliya and Ninad Bhatt, “Feature Extraction and Classification Techniques for Speaker Recognition: A Review”, IEEE international Conference on Electrical, Electronics, Signals, Communication and Optimization (EESCO), Visakhapatnam, January 2015
- Nematollahi, Mohammad Ali, S. A. R. Al-Haddad, Shyamala Doraisamy, F. Zarafshan. "Blind Digital Speech watermarking Based on Eigen-value Quantization In DWT." Journal of King Saud University, December, 2014, pp. 58-67.
- Nematollahi, Mohammad Ali, and S. A. R. AlHaddad. "An overview of digital speech watermarking." International Journal of Speech Technology, 2013, pp. 1- 18.
- Seethal Paul , Sreelakshmi T.G. , “Performance Analysis and Study of Audio Watermarking Algorithms”, International Journal Of Engineering And Computer Science ISSN:2319-7242 Volume - 3 Issue -8 August, 2014 Page No. 7540-7547
- Sathiarekha K, “A survey on the evolution of various voice conversion techniques”, Communication Systems (ICACCS), 2016 3rd International Conference on, IEEE January 2016.
- Harshita Gupta, Divya Gupta, “LPC and LPCC method of feature extraction in Speech Recognition System”, Cloud System and Big Engineering(Confluence),2016 6th International Conference, IEEE January 2016.
- Namrata Dave, “Feature Extraction Methods LPC, PLP and MFCC In Speech Recognition”, IJARET, Volume 1, Issue VI, July 2013.
- Saravanan K. and S. Sasithra,”Review on Classification Based On Artificial Neural Networks”, International Journal of Ambient System And Applications, Vol 2, No. 4, December 2014.
- Patel, Shruhad Kumar J., and Nikunj V. Tahilramani. "Information Hiding Techniques: Watermarking, Steganography: A Review."
- Nidhi Desai, Kinnal Dhameliya and Vijayendra Desai, “Recognizing voice commands for robot using MFCC and DTW”, International Journal of Advanced Research in Computer and Communication Engineering, Volume 3, Issue 5, May, 2014.
- Fang-Yie Leu, “An MFCC-Based Speaker Identification System,”, Advanced Information Networking and Applications (AINA), 2017 IEEE 31st International Conference on, March 2017
- Jianglin Wang, An Ji and Michael T. Johnson, “Features for Phoneme Independent Speaker Identification”, IEEE International Conference on Audio Language and Image Processing (ICALIP), Shanghai, July, 2012, pp. 1141-1145.
- Seiichi Nakagawa, Longbiao Wang and Shinji Ohtsuka, “Speaker Identification and Verification by Combining MFCC and Phase Information”, IEEE transaction on audio, speech and language processing, Volume 20, Issue 4, May, 2012, pp. 1085-1095. [8] Srikanth R Madikeri and Hema A Murthy, “Mel Filter Bank Energy- Based Slope Feature and Its Application to Speaker Recognition”, IEEE National Conference on communication (NCC), Bangalore, January, 2011, pp. 1-4.
- Hemant A. Patil, Purushotam G. Radadia and T. K. Basu, “Combining Evidences from Mel Cepstral Features and Cepstral Mean Subtracted Features for Singer Identification”, IEEE International Conference on Asian Language Processing, Hanoi, November, 2012, pp. 145-148.
- Nihalkumar G. Desai and Nikunj V. Tahilramani, “Speaker Recognition System Using Watermark Technology for Anti-Spoofing Attack: A Review”, International Journal of Innovative Research in Electrical, Electronics, Instrumentation and Control Engineering,Volume 4, Issue 4, April, 2016, pp. 152-156.
- R. P. Ramachandran, K.R. Farrell, R. Ramachandran and R. J. Mammone, “Speaker Recognition—General Classifier Approaches and Data Fusion Methods”, Pattern Recognition in Information Systems, Volume 35, Issue-12, December, 2002, pp. 2801-2821.
- C. J. C. Burges, “A Tutorial on Support Vector Machines for Pattern Recognition”, Data Mining and Knowledge Discovery (Springer), Volume 2, Issue-2, June, 1998, pp. 121-167.
- Ghahramani Z., “An Introduction to Hidden Markov Models and Bayesian Networks”, International Journal of Pattern Recognition and Artificial Intelligence, Volume. 5, Issue-1, 2001, pp. 9-42.