Global Trends in Speaker Identification Under Voice Disguise: A 25-Year Review
Abstract
Voice disguise is the technique by which a speaker deliberately changes their voice to conceal their identity. This poses a major challenge in the field of speaker identification. Due to these major challenges, research on disguised voices has significantly increased in security and forensic applications. Disguised voices can be exploited to evade detection in surveillance, verification, and forensic investigations, raising concerns about system reliability, evidence integrity, and overall communication security. The purpose of this study was to investigate the major countries, institutions, famous researchers, collaboration networks, high-impact publications, and key research subjects and trends in the field of speaker identification from disguised voice. This review analysis investigates the present state of speaker identification research on a global scale, with a special emphasis on disguised voice. In proposed research work shown a bibliometric analysis of 3287 manuscripts that were published between the years 2001 and 2025. The study analyses from dimensions AI dataset that covers the years 2001–2025 and uses VOS viewer to show how different countries, institutions, and authors collaborate, cite each other, and produce research in speaker identification from disguised voice. According to the citation analysis for speaker identification from disguised voice, the field is being mounded by seminal works and important researchers. Germany, UK, and US are quickly becoming the world's leading research centers. Increasing global cooperation in speaker identification from disguised voice area has led to clear geographical clustering in collaboration networks, with Europe and Asia being the most prominent. From 2025 to 2040, the forecast model predicts a consistent growth in publication production in speaker identification. As a result, efforts to develop speaker identification systems will likely continue. Citation mapping not only shows the evolution of the research landscape but also the most frequently cited foundational works and high-impact articles. This research work on speaker identification serves as a strategic resource for future research because it not only emphasizes the importance of collaborative research but also highlights influential literature, active nations, and emerging trends. For academics interested in the past, present, and future of speaker identification research from disguised voice in this exciting and rapidly evolving area of technology, this bibliometric analysis will be a priceless resource.
Keywords
References
- M. Farrús, “Voice disguise in automatic speaker recognition,” ACM Computing Surveys (CSUR), vol. 51, no. 4, pp. 1–22, 2018. doi: 10.1145/3195832
- M. K. Singh, “Identification of Speaker from Disguised Voice Using MFCC Feature Extraction, Chi-Square and Classification Technique,” Wireless Pers. Commun, vol. 138, no. 2, pp. 973–987, 2024. doi: 10.1007/s11277-024-11542-0
- L. Tavi, T. Kinnunen, and R. G. Hautamäki, “Improving speaker de-identification with functional data analysis of f0 trajectories,” Speech Commun, vol. 140, pp. 1–10, 2022. doi: 10.1016/j.specom.2022.03.010
- I. Altalahin, S. AlZu'bi, A. Alqudah, and A. Mughaid, “Unmasking the truth: A deep learning approach to detecting deepfake audio through MFCC features,” in Proc. Int. Conf. on Information Technology (ICIT), pp. 511–518, Aug. 2023. doi: 10.1109/ICIT58056.2023.10226172
- E. B. Kang, “Biometric imaginaries: Formatting voice, body, identity to data,” Soc. Stud. Sci., vol. 52, no. 4, pp. 581–602, 2022. doi: 10.1177/03063127221079599
- S. Krouwer, K. Poels, and S. Paulussen, “To disguise or to disclose? The influence of disclosure recognition and brand presence on readers' responses toward native advertisements in online news media,” J. Interact. Advert., vol. 17, no. 2, pp. 124–137, 2017. doi:10.1080/15252019.2017.1381579
- I. Park, and B. Yoon, “Identifying promising research frontiers of pattern recognition through bibliometric analysis,” Sustainability, vol. 10, no. 11, p. 4055, 2018. doi: 10.3390/su10114055
- J. Chen, and H. Chang, “Sketching the landscape of speech perception research (2000–2020): A bibliometric study,” Front. Psychol., vol. 13, p. 822241, 2022. doi: 10.3389/fpsyg.2022.822241
Details
Primary Language
English
Subjects
Software Engineering (Other)
Journal Section
Review
Authors
Early Pub Date
March 30, 2026
Publication Date
March 30, 2026
Submission Date
July 30, 2025
Acceptance Date
November 19, 2025
Published in Issue
Year 2026 Volume: 9 Number: 1
