Analysis and detection of mimicked speech based on prosodic features

International Journal of Speech Technology

Volumn 15, Issue 3, 2012, Pages 407-417

  Mary, Leena ^a   Anish Babu, K K ^b   Joseph, Aju ^b  

^a Govt Engineering College   (India)

^b RAJIV GANDHI INSTITUTE OF TECHNOLOGY   (India)

Author keywords

Analysis of mimicked speech; Intonation; Prosodic features; Speaker recognition; Support vector machines

Indexed keywords

AUTOMATIC EXTRACTION; ENERGY CONTOURS; ENERGY FEATURE; INTONATION; PROSODIC FEATURES; SPEAKER CHARACTERISTICS; SPEAKER MODEL; SPEAKER RECOGNITION; SPEAKER VERIFICATION; SPEECH DATA; SPEECH/NONSPEECH CLASSIFICATION; SVM MODEL;

SPEECH RECOGNITION;

SUPPORT VECTOR MACHINES;

EID: 84864591479     PISSN: 13812416     EISSN: 15728110     Source Type: Journal    
DOI: 10.1007/s10772-012-9163-3     Document Type: Article

References (26)

1. Adami, A. G., Mihaescu, R., Reynolds, D. A., & Godfrey, J. J. (2003). Modeling prosodic dynamics for speaker recognition. In Proceeding of int. conf. acoust., speech and signal processing, Hong Kong, China (Vol. 4, pp. 788-791).
2. Atal, B. (1972). Automatic speaker recognition based on pitch contours. The Journal of the Acoustical Society of America, 52(3), 1687-1697.
3. Blomberg, M., Elenius, D., & Zetterholm, E. (2004). Speaker verification scores and acoustic analysis of a professional impersonator. In Proceedings FONETIK 2004 the XVIIth Swedish phonetics conference (pp. 84-87).
4. Campbell, J. P. (1997). Speaker recognition: A tutorial. Proceedings of the IEEE, 85(9), 1437-1462. (Pubitemid 127745630)
5. Drygajlo, A. (2007). Forensic automatic speaker recognition. IEEE Signal Processing Magazine, 132-135. (Pubitemid 46534537)
6. Farrús, M., Wagner, M., Erro, D., & Hernando, J. (2010). Automatic speaker recognition as a measurement of voice imitation and conversion. The International Journal of Speech, Language and the Law, 17(1), 119-142.
7. Heck, L. P. (2002). Integrating high-level information for robust speaker recognition in John Hopkins University workshop on Su-perSID. Baltimore, Maryland. http://www.cslp.jhu.edu/ws2002/groups/supersid.
8. Kinnunen, T., & Li, H. (2010). An overview of text-independent speaker recognition: from features to supervectors. Speech Communication, 52, 12-40.
9. Lin, C., & Wang, H. (2005). Language identification using pitch contour information. In Proceedings of int. conf. acoust., speech and signal processing, Philadelphia. USA (Vol. I, pp. 601-605).
10. Mary, L. (2006). Multilevel implicit features for language and speaker recognition. Ph.D. Thesis, Indian Institute of Technology, Madras, India.
11. Mary, L. (2011). Prosodic features for speaker recognition. In A. Neustein & H. A. Patil (Eds.), Forensic speaker recognition- law enforcement and counter-terrorism (pp. 365-388). Berlin: Springer.
12. Mary, L., & Yegnanarayana, B. (2006). Prosodic features for speaker verification. In Proceedings of interspeech, Pittsburgh, Pennsylvania (pp. 917-920).
13. Mary, L., & Yegnanarayana, B. (2008). Extraction and representation of prosodic features for language and speaker recognition. Speech Communication, 50, 782-796.
14. Moattar, M. H., Homayounpour, M. M., & Kalantari, N. K. (2010). A new approach for robust realtime voice activity detection using spectral pattern. In Proceeding of int. conf. acoust., speech and signal processing (pp. 4478-4481).
15. NIST (2001). Speaker recognition evaluation website. http://www.nist. gov/speech/tests/spk/2001.
16. Perrot, P., & Chollet, G. (2008). The question of disguised voice. In Proceedings of acoustics 08, Paris (pp. 5681-5685).
17. Perrot, P., Aversano, G., & Chollet, G. (2007). Voice disguise and automatic detection: review and perspectives. Lecture notes in computer science.: Vol. 4391. In Progress in nonlinear speech processing (pp. 101-117). Berlin: Springer. (Pubitemid 351256463)
18. Perrot, P., Morel, M., Razik, G., & Chollet, G. (2009). Lecture notes of the Institute for Computer Sciences: Vol. 8. Vo c a l f o r g e r y i n forensic sciences, Social Informatics and Telecommunication Engineering (pp. 179-185). Berlin: Springer.
19. Rabinerl, L. R., & Schafer, R. W. (2007). Introduction to digital speech processing. Foundations and Trends in Signal Processing, 1(1-2), 1-194.
20. Reynolds, D., Andrews, W., Campbell, J., Navratil, J., Peskin, B., Adami, A., Jin, Q., Klusacek, D., Abramson, J., Mihaescu, R., Godfrey, J., Jones, D., & Xiang, B. (2003). The super-SID project: exploiting high-level information for high-accuracy speaker recognition. In Proceedings of int. conf. acoust., speech and signal processing, Hong Kong, China (Vol. 4, pp. 784-787).
21. Rose, P. (2006). Technical speaker recognition: evaluation, types and testing of evidence. Computer Speech & Language, 20, 159-191. (Pubitemid 41787535)
22. Shriberg, E., & Stolcke (2008). The case for automatic higher level features in forensic speaker recognition. In Proceedings of interspeech (pp. 1509-1512).
23. Shriberg, E., & Stolcke (2008). The case for automatic higher level features in forensic speaker recognition. In Proceedings of interspeech (pp. 1509-1512).
24. Zetterholm, E. (2006). Same speaker-different voices. A study of one impersonator and some of his different imitations. In Proceedings of the 11th Australian international conference on speech science and technology (pp. 70-75).
25. Zetterholm, E., & Sullivan, K. P. H. (2002). The impact of semantic expectation on the acceptance of a voice imitation. In Proceedings of the 9th Australian conference on speech science and technology (pp. 291-296).
26. Zetterholm, E., Blomberg, M., & Elenius, D. A. (2004). Comparison between human perception and a speaker verification system score of a voice imitation. In Proceedings of the 10th Australian international conference on speech science and technology (pp. 393-397).