Artificial intelligence in the service of voice and speech forensics
Keywords:
voice and speech, Automatic identification, biometry, forensic, artificial intelligence
Abstract
Introduction. Artificial intelligence represents the pinnacle of modern technological development. Designed to assist humans in performing tasks that are burdensome or even exceed human capabilities, this computer-based system finds application in various areas of professional and everyday functioning. One of the fields where the resources provided by artificial intelligence can be of particular value is forensic science. Objective. The aim of this paper is to present, through a review of the available literature, the potential applications of artificial intelligence in the context of voice and speech forensics, its limitations, and the potential risks associated with its use in judicial proceedings. Methods. The literature used in this study was obtained by searching accessible databases via remote access, followed by a synthesis of relevant findings. Results. Artificial intelligence is a highly sophisticated technological advancement with the potential to transform various forensic disciplines in the future. Due to the fact that language is one of the most complex social phenomena, as well as the consequences that could arise from analyses whose inner workings are not transparent to expert witnesses, this technology has not yet been included among the officially recognized and accepted methods of voice forensic analysis. Conclusion. Artificial intelligence can contribute to forensic voice and speech examinations. However, given that the lack of transparency of such systems raises fundamental ethical concerns, its application remains, for the time being, limited to operations conducted during the preparatory phase of forensic analysis. Increasing the transparency of AI-based systems, along with ongoing efforts to improve their robustness and accuracy, will help resolve ethical dilemmas and support the acceptance of this technology as legitimate within judicial proceedings.
References
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21. Jain, P., Chinmayee, P., Kaur, K., Chaudhary, S., Kaur, K., & Karunya, S. (2024a). Advancements in forensic voice analysis: Legal frameworks and technology integration. Asian Journal of Advances in Research, 7(1), 369–384. https://jasianresearch.com/index.php/AJOAIR/article/view/464
22. Jain, P., Ujjainia, P., Srivastava, A., Shrivastav, K., Rani, I., Vashisht, A., Behera, R., Moza, B., & Mukherjee, D. (2024b). Forensic perspective on voice biometrics and AI: A review. International Journal of Scientific Research in Science and Technology, 11(5), 49–63. https://doi.org/10.32628/IJSRST2411581
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24. Kamble, Y., & Devarmani, N. G. (2024). Application of artificial intelligence (AI) in forensic investigation. In R. Mia, V. Panchal, S. Singla, & M. Jain (Eds.), Advanced and emerging development in multidisciplinary approaches in forensic science, digital forensic, law and criminology (pp. 197–204). AkiNik Publications. https://doi.org/10.22271/ed.book.2825
25. Kassem, M. A., & Lodhi, K. (2024). Revolutionizing forensic science: The role of artificial intelligence and machine learning. International Journal of Data Science, vi, 10–18. https://doi.org/10.5147/ijds.vi.255
26. Kovačević, J., & Šešum, M. (2012). Uticaj mehaničke modifikacije govora na akustičku strukturu govornih segmenata. U Zbornik radova 56. konferencije ETRAN (str. 1–4). Zlatibor, Srbija: Društvo za ETRAN.
27. Mitchell, F. (2014). The use of artificial intelligence in digital forensics: An introduction. Digital Evidence and Electronic Signature Law Review, 7. https://doi.org/10.14296/deeslr.v7i0.1922
28. Morrison, G. S., Enzinger, E., Ramos, D., González-Rodríguez, J., & Lozano-Díez, A. (2020). Statistical models in forensic voice comparison. In D. L. Banks, K. Kafadar, D. H. Kaye, & M. Tackett (Eds.), Handbook of forensic statistics (Ch. 20, pp. 451–497). CRC Press.
29. Negrão, M., & Domingues, P. (2021). SpeechToText: An open-source software for automatic detection and transcription of voice recordings in digital forensics. Forensic Science International: Digital Investigation, 38, 301223. https://doi.org/10.1016/j.fsidi.2021.301223
30. O'Hear, M. M. (2019). Appellate review of sentence explanations: Learning from the Wisconsin and federal experiences. Marquette Law Review, 103(2), 641–678. https://scholarship.law.marquette.edu/mulr/vol93/iss2/15/?utm_source=chatgpt.com
31. Patil, H. A., & Parhi, K. K. (2009). Variable length Teager energy based Mel cepstral features for identification of twins. In S. Chaudhury, A. N. Rajagopalan, D. S. Chauhan, S. Murty, & P. K. Biswas (Eds.), Pattern Recognition and Machine Intelligence (pp. 525–530). Springer. https://doi.org/10.1007/978-3-642-11164-8_85
32. Phonexia launches voice biometrics product for forensic investigations. (2024, January 1). Biometric Update. https://www.biometricupdate.com/202401/phonexia-launches-voice-biometrics-product-for-forensic-investigations
33. Ribeiro, M. T., Singh, S., & Guestrin, C. (2016). "Why should I trust you?": Explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 1135–1144). ACM. https://doi.org/10.1145/2939672.2939778
34. Sal, F. (2019). Biometric techniques for personal identification & voice authentication. ResearchGate. https://doi.org/10.13140/RG.2.2.16351.61607
35. Singh, N., Agrawal, A., & Khan, R. A. (2018). Voice biometric: A technology for voice-based authentication. Advanced Science, Engineering and Medicine, 10(7–8), 754–759. https://doi.org/10.1166/asem.2018.2219
36. Singh, S. (2019). The role of speech technology in biometrics, forensics and man-machine interface. International Journal of Electrical and Computer Engineering, 9(1), 281–288. https://doi.org/10.11591/ijece.v9i1.pp281-288
37. Slovak, B. L., & Shepherd, S. M. (2020). Machine learning and forensic risk assessment: New frontiers. The Journal of Forensic Psychiatry & Psychology, 31(4), 571–581. https://doi.org/10.1080/14789949.2020.1779783
38. Susanto, S., & Nanda, D. S. (2024). Predicting the classification of high vowel sound by using artificial neural network: A study in forensic linguistics. IAES International Journal of Artificial Intelligence (IJ-AI), 13(1), 195–200. https://doi.org/10.11591/ijai.v13.i1.pp195-200
39. Šešum, M. (2013). Komparativna analiza formantnih struktura glasova sestara i glasova monozigotnih bliznakinja. Beogradska defektološka škola, 19(3), 515–527.
40. Šešum, M. (2021). Forenzička fonetika-identifikacija govornika. U: Knežević, S. (ur.), Forenzičko računovodstvo, istražne radnje, ljudski faktor i primenjeni alati (str. 828- 859). Beograd: Fakultet organizacionih nauka.. ISBN: 978-86-7680-393-4
41. Šešum, M. (2022). Uloga surdologa i logopeda u forenzičkoj analizi glasa i govora. V simpozijum logopeda Srbije: U Timski rad u logopediji i defektologijii (str. 280-290). Udruženje logopeda Srbije.
42. Šešum, M. (2024). Forenzički značaj maskiranja glasa i govora. U Zbornik sažetaka radova IX simpozijuma logopeda Srbije: Transdisciplinarnost u logopediji danas (str. 11). Udruženje logopeda Srbije. ISBN 978-86-904732-2-9.
43. Šešum, M., Kovačević, J. (2015). Forenzička fonetika. Vodič za primenu Zakonika o krivičnom postupku Republike Srbije (str. 90–105). Grafol
44. Šešum, M., & Petrović, E. (2024). The complexity of speaker identification based on voice and speech and its application in forensics. In S. H. Fazeli (Ed.), The Ninth International Conference on Languages, Linguistics, Translation and Literature – Full Articles (Volume One) (pp. 90-108), Ahwaz Publication of Research and Sciences. ISBN 978-622-90390-9-0
45. Tistarelli, M., Grosso, E., & Meuwly, D. (2014). Biometrics in forensic science: Challenges, lessons and new technologies. U Proceedings of the International Workshop on Biometric Authentication (pp. 153–164). Springer. https://doi.org/10.1007/978-3-319-05050-8_15
46. Watanabe, S., Delcroix, M., Metze, F., & Hershey, J. R. (Eds.). (2017). New era for robust speech recognition. Springer International Publishing. https://doi.org/10.1007/978-3-319-64680-0
2. Al Ajmi, S., Hayat, K., Al Obaidi, A. M., Kumar, N., Najmuldeen, M., & Magnier, B. (2022). Digital audio forensics: Blind human voice mimicry detection. arXiv preprint arXiv:2209.12573. https://doi.org/10.48550/arXiv.2209.12573
3. Alam, S., & Altiparmak, Z. (2024). XAI-CF - Examining the role of explainable artificial intelligence in cyber forensics. arXiv. https://doi.org/10.48550/arXiv.2402.02452
4. Anirudh, V. K. (2019, October 17). What Is Deep Learning: Definition, Framework, and Neural Networks [Web log post]. Retrieved July 12, 2020, from https://www.toolbox.com/tech/artificial-intelligence/tech-101/what-is-deep-learning-definition-framework-and-neural-networks/
5. Arjamand, M., Saleem, A., Basit, A., Iftikhar, S., Sharif, M., Cholistani, M. S., Farhan, M., Shumail, S., Khan, B. A., Ali, Z., Shahid, B., & Hasnain, M. (2024). The role of artificial intelligence in forensic science: Transforming investigations through technology. International Journal of Multidisciplinary Research and Publications (IJMRAP), 7(5), 67–70. https://ijmrap.com/wp-content/uploads/2024/10/IJMRAP-V7N5P52Y24.pdf
6. Ben Amor, I. (2024). Deep modeling based on voice attributes for explainable speaker recognition: Application in the forensic domain [Doctoral dissertation, Université d’Avignon]. HAL Archives. https://hal.science/tel-04634215
7. Bitter, C., Elizondo, D. A., & Watson, T. (2010). Application of artificial neural networks and related techniques to intrusion detection. IEEE World Congress on Computational Intelligence (WCCI 2010), 949–954. https://doi.org/10.1109/IJCNN.2010.5596532
8. Bonastre, J. F., Campbell, J. P., & Mason, J. S. D. (2010). Forensic speaker recognition. IEEE Signal Processing Magazine, 27(2), 95–103. https://doi.org/10.1109/MSP.2010.936730
9. de Lima, T. A., & Da-Costa Abreu, M. C. (2022). Phoneme analysis for multiple languages with fuzzy-based speaker identification. IET Biometrics, 11(6), 614–624. https://doi.org/10.1049/bme2.12078
10. Dilek, S., Çakır, H., & Aydın, M. (2015). Applications of artificial intelligence techniques to combating cyber crimes: A review. International Journal of Artificial Intelligence & Applications (IJAIA), 6(1), 21–39. https://doi.org/10.5121/ijaia.2015.6102
11. Dunsin, D., Ghanem, M. C., & Ouazzane, K. (2022). The use of artificial intelligence in digital forensics and incident response in a constrained environment. International Journal of Information and Communication Engineering, 16(8), 280–28
12. Durán, J. M., van der Vloed, D., Ruifrok, A., & Ypma, R. J. F. (2024). From understanding to justifying: Computational reliabilism for AI-based forensic evidence evaluation. Forensic Science International: Synergy, 9, 100554. https://doi.org/10.1016/j.fsisyn.2024.100554
13. Endah, S. N., Adhy, S., & Sutikno, S. (2017). Comparison of feature extraction MFCC and LPC in automatic speech recognition for Indonesian. TELKOMNIKA (Telecommunication Computing Electronics and Control), 15(1), 292–298. https://doi.org/10.12928/telkomnika.v15i1.3605
14. Franke, K. (2023). Artificial intelligence (AI) in forensic sciences. Wiley-Blackwell.
15. Franke, K., & Srihari, S. N. (2007, August). Computational forensics: Towards hybrid-intelligent crime investigation. In Third International Symposium on Information Assurance and Security (pp. 383-386). IEEE.
16. Hall, S. W., Sakzad, A., & Choo, K. R. (2021). Explainable artificial intelligence for digital forensics. WIREs Forensic Science, 4(2), 1–11. https://doi.org/10.1002/wfs2.1434
17. Hoelz, B., Ralha, C., & Geeverghese, R. (2009). Artificial intelligence applied to computer forensics. U Proceedings of the 2009 ACM Symposium on Applied Computing - SAC '09 (pp. 1181–1185). https://doi.org/10.1145/1529282.1529549
18. Ivanović, M., & Šešum, M. (2009). Jedan tip regionalne redukcije neakcentovanih slogova kao forenzički marker. U Zbornik radova 53. konferencije ETRAN (str. 1–4). Vrnjačka Banja, Srbija: Društvo za ETRAN.
19. Jadhav, E. B., Sankhla, M. S., & Kumar, R. (2020). Artificial intelligence: Advancing automation in forensic science & criminal investigation. Journal of Seybold Report, 15(8), 2064–2075. https://doi.org/10.2174/2666484401666220819111603
20. Jain, A. K., & Kumar, A. (2010). Biometrics of next generation: An overview. Second Generation Biometrics, 12(1), 2–3.
21. Jain, P., Chinmayee, P., Kaur, K., Chaudhary, S., Kaur, K., & Karunya, S. (2024a). Advancements in forensic voice analysis: Legal frameworks and technology integration. Asian Journal of Advances in Research, 7(1), 369–384. https://jasianresearch.com/index.php/AJOAIR/article/view/464
22. Jain, P., Ujjainia, P., Srivastava, A., Shrivastav, K., Rani, I., Vashisht, A., Behera, R., Moza, B., & Mukherjee, D. (2024b). Forensic perspective on voice biometrics and AI: A review. International Journal of Scientific Research in Science and Technology, 11(5), 49–63. https://doi.org/10.32628/IJSRST2411581
23. Jansen, F., Sánchez-Monedero, J., & Dencik, L. (2021). Biometric identity systems in law enforcement and the politics of (voice) recognition: The case of SiiP. Big Data & Society, 8(2), 49–63. https://doi.org/10.1177/20539517211063604
24. Kamble, Y., & Devarmani, N. G. (2024). Application of artificial intelligence (AI) in forensic investigation. In R. Mia, V. Panchal, S. Singla, & M. Jain (Eds.), Advanced and emerging development in multidisciplinary approaches in forensic science, digital forensic, law and criminology (pp. 197–204). AkiNik Publications. https://doi.org/10.22271/ed.book.2825
25. Kassem, M. A., & Lodhi, K. (2024). Revolutionizing forensic science: The role of artificial intelligence and machine learning. International Journal of Data Science, vi, 10–18. https://doi.org/10.5147/ijds.vi.255
26. Kovačević, J., & Šešum, M. (2012). Uticaj mehaničke modifikacije govora na akustičku strukturu govornih segmenata. U Zbornik radova 56. konferencije ETRAN (str. 1–4). Zlatibor, Srbija: Društvo za ETRAN.
27. Mitchell, F. (2014). The use of artificial intelligence in digital forensics: An introduction. Digital Evidence and Electronic Signature Law Review, 7. https://doi.org/10.14296/deeslr.v7i0.1922
28. Morrison, G. S., Enzinger, E., Ramos, D., González-Rodríguez, J., & Lozano-Díez, A. (2020). Statistical models in forensic voice comparison. In D. L. Banks, K. Kafadar, D. H. Kaye, & M. Tackett (Eds.), Handbook of forensic statistics (Ch. 20, pp. 451–497). CRC Press.
29. Negrão, M., & Domingues, P. (2021). SpeechToText: An open-source software for automatic detection and transcription of voice recordings in digital forensics. Forensic Science International: Digital Investigation, 38, 301223. https://doi.org/10.1016/j.fsidi.2021.301223
30. O'Hear, M. M. (2019). Appellate review of sentence explanations: Learning from the Wisconsin and federal experiences. Marquette Law Review, 103(2), 641–678. https://scholarship.law.marquette.edu/mulr/vol93/iss2/15/?utm_source=chatgpt.com
31. Patil, H. A., & Parhi, K. K. (2009). Variable length Teager energy based Mel cepstral features for identification of twins. In S. Chaudhury, A. N. Rajagopalan, D. S. Chauhan, S. Murty, & P. K. Biswas (Eds.), Pattern Recognition and Machine Intelligence (pp. 525–530). Springer. https://doi.org/10.1007/978-3-642-11164-8_85
32. Phonexia launches voice biometrics product for forensic investigations. (2024, January 1). Biometric Update. https://www.biometricupdate.com/202401/phonexia-launches-voice-biometrics-product-for-forensic-investigations
33. Ribeiro, M. T., Singh, S., & Guestrin, C. (2016). "Why should I trust you?": Explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 1135–1144). ACM. https://doi.org/10.1145/2939672.2939778
34. Sal, F. (2019). Biometric techniques for personal identification & voice authentication. ResearchGate. https://doi.org/10.13140/RG.2.2.16351.61607
35. Singh, N., Agrawal, A., & Khan, R. A. (2018). Voice biometric: A technology for voice-based authentication. Advanced Science, Engineering and Medicine, 10(7–8), 754–759. https://doi.org/10.1166/asem.2018.2219
36. Singh, S. (2019). The role of speech technology in biometrics, forensics and man-machine interface. International Journal of Electrical and Computer Engineering, 9(1), 281–288. https://doi.org/10.11591/ijece.v9i1.pp281-288
37. Slovak, B. L., & Shepherd, S. M. (2020). Machine learning and forensic risk assessment: New frontiers. The Journal of Forensic Psychiatry & Psychology, 31(4), 571–581. https://doi.org/10.1080/14789949.2020.1779783
38. Susanto, S., & Nanda, D. S. (2024). Predicting the classification of high vowel sound by using artificial neural network: A study in forensic linguistics. IAES International Journal of Artificial Intelligence (IJ-AI), 13(1), 195–200. https://doi.org/10.11591/ijai.v13.i1.pp195-200
39. Šešum, M. (2013). Komparativna analiza formantnih struktura glasova sestara i glasova monozigotnih bliznakinja. Beogradska defektološka škola, 19(3), 515–527.
40. Šešum, M. (2021). Forenzička fonetika-identifikacija govornika. U: Knežević, S. (ur.), Forenzičko računovodstvo, istražne radnje, ljudski faktor i primenjeni alati (str. 828- 859). Beograd: Fakultet organizacionih nauka.. ISBN: 978-86-7680-393-4
41. Šešum, M. (2022). Uloga surdologa i logopeda u forenzičkoj analizi glasa i govora. V simpozijum logopeda Srbije: U Timski rad u logopediji i defektologijii (str. 280-290). Udruženje logopeda Srbije.
42. Šešum, M. (2024). Forenzički značaj maskiranja glasa i govora. U Zbornik sažetaka radova IX simpozijuma logopeda Srbije: Transdisciplinarnost u logopediji danas (str. 11). Udruženje logopeda Srbije. ISBN 978-86-904732-2-9.
43. Šešum, M., Kovačević, J. (2015). Forenzička fonetika. Vodič za primenu Zakonika o krivičnom postupku Republike Srbije (str. 90–105). Grafol
44. Šešum, M., & Petrović, E. (2024). The complexity of speaker identification based on voice and speech and its application in forensics. In S. H. Fazeli (Ed.), The Ninth International Conference on Languages, Linguistics, Translation and Literature – Full Articles (Volume One) (pp. 90-108), Ahwaz Publication of Research and Sciences. ISBN 978-622-90390-9-0
45. Tistarelli, M., Grosso, E., & Meuwly, D. (2014). Biometrics in forensic science: Challenges, lessons and new technologies. U Proceedings of the International Workshop on Biometric Authentication (pp. 153–164). Springer. https://doi.org/10.1007/978-3-319-05050-8_15
46. Watanabe, S., Delcroix, M., Metze, F., & Hershey, J. R. (Eds.). (2017). New era for robust speech recognition. Springer International Publishing. https://doi.org/10.1007/978-3-319-64680-0
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