Forensic voice comparison by means of artificial neural networks

• Autorzy: Sałapa K , Trawińska A. , Roterman-Konieczna I.
• Języki publikacji: EN

Abstrakty

EN

This article examines the effectiveness of artificial neural networks (ANNs) as forensic voice comparison techniques. This study specifically considers feed-forward multilayer perceptron (MLP) and radial basic function (RBF) network models. Formant frequencies of Polish vowel e (stressed or unstressed) in selected contexts were used as predictors. This has already been confirmed in an earlier investigation that determined that dynamic formant frequencies of vowels are powerful elements in distinguishing the voice. It has been concluded that neural networks might assist in distinguishing speakers from the others with very good accuracy, reaching 100%. MLP models should be given preference. The results of the investigation have shown the influence of vowel e triads on the effectiveness of correct classification rates. In addition, the authors have determined that the accuracy of classification is greater when based on a single context than for similar input data aggregated over several different contexts.

Słowa kluczowe

EN

artificial neural networks; data mining; forensic speaker identification; forensic voice comparison; formant frequencies

• Wydawca: Uniwersytet Jagielloński - Collegium Medicum ; Index Copernicus Sp. z o.o.
• Czasopismo: Bio-Algorithms and Med-Systems
• Rocznik: 2013
• Tom: Vol. 9, no. 4
• Strony: 191--197
• Opis fizyczny: Bibliogr. 23 poz., tab., wykr.

Twórcy

autor

Sałapa K

• Department of Bioinformatics and Telemedicine, Jagiellonian University Medical College, św. Łazarza 16, 31-530 Kraków, Poland

autor

Trawińska A.

• Institute of Forensic Research, Kraków, Poland

autor

Roterman-Konieczna I.

• Department of Bioinformatics and Telemedicine, Jagiellonian University Medical College, Kraków, Poland

Bibliografia

• 1. Enzinger E. Formant trajectories in forensic speaker recognition. PhD dissertation. Wien: Universität in Wien, 2009.
• 2. Drygajlo A. Value and interpretation of biometric evidence in forensic automatic speaker recognition, 2010. Available at: http://cancun2010.forensic-voice-comparison.net. Accessed: 30 Jul 2013.
• 3. Trawińska A, Klus A. Forensic speaker identification by the linguistic-acoustic method in KEU and IES. Problems Forensic Sci 2009;LXXVIII:160-74.
• 4. Fant G. Acoustic theory of speech production. Mouton: The Hague, 1960.
• 5. Nolan F. Speaker identification evidence: its forms, limitations and roles. In: Proceedings of the Conference Law and Language: Prospect and Retrospect, University of Texas School of Law 2001:1-19.
• 6. Künzel H. Effects of voice disguise on speaking fundamental frequency. Forensic Linguist 2000;7:149-79.
• 7. Suneetha DG. Pitch breaks as voice disguise. In: Proceedings of 22nd Conference of the International Association for Forensic Phonetics and Acoustics, July 21-24, 2013, University of South Florida, USA, 2013.
• 8. Masthoff H, Meinerz Ch. The effectiveness of voice disguise: implications for research and casework. In: Proceedings of the 22nd Conference of the International Association for Forensic Phonetics and Acoustic, July 21-24, 2013, University of South Florida, USA, 2013.
• 9. Nolan F, Grigoras C. A case for formant analysis in forensic speaker identification. Int J Speech Lang Law 2005;2:143-73.
• 10. Gold E, French P. International practices in forensic speaker comparison. Int J Speech Lang Law 2011;18:293-307.
• 11. McDougall K. Speaker-specific formant dynamics: an experiment on Australian English /al/. Int J Speech Lang Law 2004;ll:103-30.
• 12. McDougall K, Nolan F. Discrimination of speakers using the formant dynamics of /u:/ in British English. In: Proceedings of the 16th International Congress of Phonetic Sciences, Universität des Saarlandes, 2007:1825-8.
• 13. McLachlan G. Discriminant analysis and statistical pattern recognition. In: Wiley series in probability and statistics. Hoboken, NJ: Wiley, 2004.
• 14. Huberty CJ, Olejnik S. Applied MANOVA and discriminant analysis. In: Wiley series in probability and statistics. Hoboken, NJ: Wiley, 2006.
• 15. Jassem W, Grygiel W. Off-line classification of Polish vowel spectra using artificial neural networks. J Int Phonetic Assoc 2004;34:37-52.
• 16. Du H. Data mining techniques and applications: an introduction. Hampshire: Cengage Learning, 2010.
• 17. Tufféry S. Data mining and statistics for decision making. In: Wiley series in computational statistics. Hoboken, NJ: Wiley, 2011.
• 18. Salapa K, Trawińska A, Roterman I. Forensic speaker identification models based on artificial neural networks. Case study: Polish vowel e. In: Annual Conference of the International Association for Forensic Phonetics and Acoustics (IAFPA), University of South Florida, USA, 2013.
• 19. Salapa K, Trawińska A, Roterman I. Applying data mining classification techniques to speaker identification, In: Proceedings of the XIX National Conference on Application of Mathematics in Biology and Medicine, September 16-20, 2013, University of Gdansk, 2013.
• 20. Bishop CM. Pattern recognition and machine learning. New York: Springer, 2006.
• 21. Basu JK, Bhattacharyya D, Kim T. Use of artificial neural network in pattern recognition. Int J Software Eng Appl 2010;4:23-34.
• 22. Jessen M. Phonetisch und linguistische Prinzipien des forensischen Stimmvergleichs. München: LINCOM EUROPA, 2012.
• 23. Jessen M. Forensic phonetics. Lang Linguist Compass 2008;2:671-711.