KaydedilenlerDerginizi ekleyin

Dergiler / Sakarya University Journal of Computer and Information Sciences / 2026 / Volume: 9 Issue: 1

Global Trends in Speaker Identification Under Voice Disguise: A 25-Year Review

Mahesh K. Singh(Aditya University, Surampalem)0009-0006-5036-6037
Sanjeev Kumar(Aditya University)0000-0001-7997-0111
Rajeev Ranjan(Chandigarh University)0000-0003-2359-4879
Sayfa
—
Yayın Tarihi
2026-03-30
🏛️Bu makalenin bibliyografik verileri ve tam metin bağlantısı DergiPark üzerinden sağlanmıştır.
DergiPark Kaydı ↗
✨

Yapay Zeka Hızlı Bakış & Çıkarımlar

AI Insight
⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

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.

📌 3 Maddede Bu Makale

🎯
Amaç & Hipotez:Voice disguise is the technique by which a speaker deliberately changes their voice to conceal their identity.
📊
Temel Bulgular & Veri:Due to these major challenges, research on disguised voices has significantly increased in security and forensic applications.
💡
Sonuç & Katkı: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.
🔬Tür:Literatür & Derleme Çalışması
📍Kapsam:2001 Verileri
⚙️Değişkenler:Speaker identification · Disguise voice · Masked speech

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.

🔖

Bu Makaleye Atıf Yap

Singh, M. K., Kumar, S., Ranjan, R. (2026). Global Trends in Speaker Identification Under Voice Disguise: A 25-Year Review. Sakarya University Journal of Computer and Information Sciences, Volume: 9 Issue: 1. https://doi.org/10.35377/saucis...1754132

📄 Tam Metin PDF Okuyucu

📄

PDF görüntüleyici hazırlanıyor...

Kaynakça (77)

  1. M. Farrús, “Voice disguise in automatic speaker recognition,” ACM Computing Surveys (CSUR), vol. 51, no. 4, pp. 1–22, 2018. doi: 10.1145/3195832
  2. 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
  3. 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
  4. 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
  5. 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
  6. 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
  7. 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
  8. 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
  9. N. Das, and N. Padhy, “A Bibliometric and co-occurrence analysis of speech processing literature published from the year 2015 to mid of June 2021,” in Proc. Int. Conf. Data Sci., Mach. Learn. Artif. Intell., pp. 237–243, 2021. doi: 10.1145/3484824.348490
  10. J. Yin, “Research on the application of visual maps based on CiteSpace in speech technology,” J. Phys.: Conf. Ser., vol. 1952, no. 2, p. 022062, 2021. doi: 10.1088/1742-6596/1952/2/022062
  11. L. Wu, and M. Chen, “A Bibliometric Analysis of Intelligent Voice Interaction Based on VOSviewer,” in Proc. Int. Conf. Human-Comput. Interact., pp. 443–455, 2022. doi: 10.1007/978-3-031-06018-2_31
  12. C. Puig-Herreros, J. L. Sanz, V. Rosell-Clari, L. Barona, and M. Melo, “What are the contemporary trends on euphonic voice research? A scientometric analysis,” Healthcare, vol. 10, no. 11, p. 2137, 2022. doi: 10.3390/healthcare10112137
  13. A. Alduais, M. A. Al-Khulaidi, S. Allegretta, and M. M. Abdulkhalek, “Forensic linguistics: A scientometric review,” Cogent Arts & Humanities, vol. 10, no. 1, Art. No. 2214387, 2023. doi: 10.1080/23311983.2023.2214387
  14. M. S. Vitevitch, “Speech, language, and hearing in the 21st century: A bibliometric review of JSLHR from 2001 to 2021,” J. Speech Lang. Hear. Res., vol. 66, no. 9, pp. 3428–3451, 2023. doi: 10.1044/2023_JSLHR-23-00203
  15. D. F. Kponou, F. A. Laleye, and E. C. Ezin, “Systematic literature review and bibliometric analysis of low-resource speech-to-text translation,” in Proc. Future Technologies Conf., Cham: Springer, pp. 379–398, Nov. 2024. doi: 10.1007/978-3-031-73125-9_24
  16. M. Wasi, S. Zaki, and R. H. Anwar, “A bibliometric analysis of research in forensic linguistics,” SAGE Open, vol. 14, no. 4, 2024. doi: 10.1177/21582440241299280
  17. M. Yan, and X. Wu, “Prosody in linguistic journals: a bibliometric analysis,” Humanit. Soc. Sci. Commun., vol. 11, Art. No. 311, pp. 1–15, 2024. doi: 10.1057/s41599-024-02825-9
  18. N. Malik, A. Singh, and A. Kumar, “Acoustic signal processing and communications: Topic modeling of scopus abstracts from 2010 till 2024,” in Proc. 27th Int. Symp. Wireless Pers. Multimedia Commun. (WPMC), pp. 1–5, Nov. 2024. doi: 10.1109/WPMC63271.2024.10863719
  19. A. Bolakale Abdul Qudus et al., “A contemporary and comprehensive bibliometric exposition on deepfake research and trends,” Comput. Mater. Continua, vol. 84, no. 1, 2025. doi: 10.32604/cmc.2025.061427
  20. M. Gee-Whai Kho, and S. H. Ting, “Bibliometric analysis of trends in research on communication apprehension,” Pertanika J. Soc. Sci. Humanit, vol. 33, no. 2, 2025. doi: 10.47836/pjssh.33.2.02
  21. G. Lemaitre, O. Houix, F. Voisin, N. Misdariis, and P. Susini, “Comparing identification of vocal imitations and computational sketches of everyday sounds,” J. Acoust. Soc. Am., vol. 140, no. 4, p. 3390, 2016. doi: 10.1121/1.4970854
  22. S. C. Dougherty, D. E. Mclaughlin, and T. K. Perrachione, “A language familiarity effect for talker identification in forward but not time-reversed speech,” J. Acoust. Soc. Am., vol. 137, no. 4, p. 2415, 2015, doi: 10.1121/1.4920807
  23. M. Ruble et al., “Hyperdimensional Bayesian time mapping (HYPERBAT): A probabilistic approach to time series mapping of non-identical sequences,” IEEE Trans. Signal Process., vol. 67, no. 14, pp. 3719–3731, 2019. doi: 10.1109/TSP.2019.2919002
  24. E. S. Hassan et al., “Enhancing speaker identification through reverberation modeling and cancelable techniques using ANNs,” PLOS ONE, vol. 19, no. 2, p. e0294235, 2024. doi: 10.1371/journal.pone.0294235
  25. N. Mamun, R. Ghosh, and J. H. Hansen, “Familiar and unfamiliar speaker recognition assessment and system emulation for cochlear implant users,” J. Acoust. Soc. Am., vol. 153, no. 2, pp. 1293–1306, 2023. doi: 10.1121/10.0017216
  26. J. H. Hansen, M. K. Nandwana, and N. Shokouhi, “Analysis of human scream and its impact on text-independent speaker verification,” J. Acoust. Soc. Am., vol. 141, no. 4, pp. 2957–2967, 2017. doi: 10.1121/1.4979337
  27. E. Hernández-García et al., “A voice and speech corpus of patients who underwent upper airway surgery in pre-and post-operative states,” Scientific Data, vol. 11, no. 1, p. 746, 2024. doi: 10.1038/s41597-024-03540-5
  28. E. Jacewicz, R. A. Fox, and C. E. Holt, “Dialect and gender perception in relation to the intelligibility of low-pass and high-pass filtered spontaneous speech,” J. Acoust. Soc. Am., vol. 154, no. 3, pp. 1667–1683, 2023. doi: 10.1121/10.0020906
  29. Y. Suh, and H. Kim, “Minimum classification error-based weighted support vector machine kernels for speaker verification,” J. Acoust. Soc. Am., vol. 133, no. 4, pp. EL307–EL313, 2013. doi: 10.1121/1.4794350
  30. I. M. S. Bimantara et al., “Balinese story texts dataset for narrative text analyses,” Data in Brief, vol. 56, p. 110781, 2024. doi: 10.1016/j.dib.2024.110781
  31. M. Zolnoori et al., “Is the patient speaking or the nurse? Automatic speaker type identification in patient–nurse audio recordings,” J. Am. Med. Inform. Assoc., vol. 30, no. 10, pp. 1673–1683, 2023. doi: 10.1093/jamia/ocad139
  32. A. B. Nassif, I. Shahin, and N. Nemmour, “Emotional speaker identification using PCAFCM-deepforest with fuzzy logic,” Neural Comput. Appl., vol. 36, no. 30, pp. 18567–18581, 2024. doi: 10.1007/s00521-024-10154-w
  33. N. M. Almarshady, A. A. Alashban, and Y. A. Alotaibi, “Analysis and investigation of speaker identification problems using deep learning networks and the YOHO English speech dataset,” Appl. Sci., vol. 13, no. 17, p. 9567, 2023. doi: 10.3390/app13179567
  34. O. H. Anidjar, R. Marbel, and R. Yozevitch, “Harnessing the power of wav2vec2 and CNNs for robust speaker identification on the VoxCeleb and LibriSpeech datasets,” Expert Syst. Appl., vol. 255, p. 124671, 2024. doi: 10.1016/j.eswa.2024.124671
  35. H. J. Künzel, “Non-contemporary speech samples: auditory detectability of an 11-year delay and its effect on automatic speaker identification,” Int. J. Speech Lang. Law, vol. 14, no. 1, pp. 109–136, 2007. doi: 10.1558/ijsll.v14i1.109
  36. A. A. Moura, N. Nepomuceno, and V. Furtado, “Enhancing speaker identification in criminal investigations through clusterization and rank-based scoring,” Forensic Sci. Int.: Digit. Investig., vol. 49, p. 301765, 2024. doi: 10.1016/j.fsidi.2024.301765
  37. M. K. Singh, “Feature extraction and classification efficiency analysis using machine learning approach for speech signal,” Multimedia Tools Appl., vol. 83, no. 16, pp. 47069–47084, 2024. doi: 10.1007/s11042-023-17368-5
  38. P. Aneja, S. K. Choudhary, and S. Mathur, “Aural-acoustic analysis and gender identification of morphed male and female audios,” Int. J. Electron. Secur. Digit. Forensics, vol. 15, no. 2, pp. 177–183, 2023. doi: 10.1504/IJESDF.2023.129275
  39. K. Daqrouq, “Wavelet entropy and neural network for text-independent speaker identification,” Eng. Appl. Artif. Intell., vol. 24, no. 5, pp. 796–802, 2011. doi: 10.1016/j.engappai.2011.01.001
  40. A. B. Kain et al., “Improving the intelligibility of dysarthric speech,” Speech Commun., vol. 49, no. 9, pp. 743–759, 2007. doi: 10.1016/j.specom.2007.05.001
  41. S. A. Kumar, and C. S. Kumar, “Improving the intelligibility of dysarthric speech towards enhancing the effectiveness of speech therapy,” in Proc. Int. Conf. Adv. Comput., Commun. Informat. (ICACCI), pp. 1000–1005, Sept. 2016. doi: 10.1109/ICACCI.2016.7732175
  42. M. Newman, and A. Wu, “‘Do you sound Asian when you speak English?’ Racial identification and voice in Chinese and Korean Americans' English,” Amer. Speech, vol. 86, no. 2, pp. 152–178, 2011. doi: 10.1215/00031283-1336992
  43. J. H. Hansen, and H. Bořil, “On the issues of intra-speaker variability and realism in speech, speaker, and language recognition tasks,” Speech Commun., vol. 101, pp. 94–108, 2018. doi: 10.1016/j.specom.2018.05.004
  44. D. Mücke et al., “Challenges with the kinematic analysis of neurotypical and impaired speech: Measures and models,” J. Phonetics, vol. 102, p. 101292, 2024. doi: 10.1016/j.wocn.2023.101292
  45. M. Corrales-Astorgano et al., “Analysis of atypical prosodic patterns in the speech of people with Down syndrome,” Biomed. Signal Process. Control, vol. 69, p. 102913, 2021. doi: 10.1016/j.bspc.2021.102913
  46. S. G. Lambacher et al., “The effects of identification training on the identification and production of American English vowels by native speakers of Japanese,” Appl. Psycholinguistics, vol. 26, no. 2, pp. 227–247, 2005. doi: 10.1017/S0142716405050150
  47. M. Cao, P. I. Pavlik Jr, and G. M. Bidelman, “Enhancing lexical tone learning for second language speakers: effects of acoustic properties in Mandarin tone perception,” Front. Psychol., vol. 15, p. 1403816, 2024. doi: 10.3389/fpsyg.2024.1403816
  48. G. Zellou, A. Pycha, and M. Cohn, “The perception of nasal coarticulatory variation in face-masked speech,” J. Acoust. Soc. Am., vol. 153, no. 2, pp. 1084–1093, 2023. doi: 10.1121/10.0017257
  49. L. Ménard, P. Trudeau-Fisette, and M. K. Tiede, “Intelligibility of speech produced by sighted and blind adults,” PLoS One, vol. 17, no. 9, Art. No. e0272127, 2022. doi: 10.1371/journal.pone.0272127
  50. K. Bunton, “Fundamental frequency as a perceptual cue for vowel identification in speakers with Parkinson’s disease,” Folia Phoniatr. Logop., vol. 58, no. 5, pp. 323–339, 2006. doi: 10.1159/000094567
  51. M. L. Ng, and R. Chu, “An acoustical and perceptual study of vowels produced by alaryngeal speakers of Cantonese,” Folia Phoniatr. Logop, vol. 61, no. 2, pp. 97–104, 2009. doi: 10.1159/000209272
  52. C. Y. Lee et al., “Effects of speaker variability and noise on Mandarin fricative identification by native and non-native listeners,” J. Acoust. Soc. Am., vol. 132, no. 2, pp. 1130–1140, 2012. doi: 10.1121/1.4730883
  53. I. Phillips et al., “Age impacts speech-in-noise recognition differently for nonnative and native listeners,” J. Speech Lang. Hear. Res., vol. 67, no. 5, pp. 1602–1623, 2024. doi: 10.1044/2024_JSLHR-23-00470
  54. S. R. Cox, L. J. Raphael, and P. C. Doyle, “Production of vowels by electrolaryngeal speakers using clear speech,” Folia Phoniatr. Logop., vol. 72, no. 4, pp. 250–256, 2020. doi: 10.1159/000499928
  55. J. K. Y. Ma, T. L. Whitehill, and S. Y. S. So, “Intonation contrast in Cantonese speakers with hypokinetic dysarthria associated with Parkinson’s disease,” J. Speech Lang. Hear. Res., vol. 53, no. 4, pp. 836–849, 2010. doi: 10.1044/1092-4388(2009/08-0216)
  56. V. G. Skuk, and S. R. Schweinberger, “Gender differences in familiar voice identification,” Hearing Res., vol. 296, pp. 131–140, 2013. doi: 10.1016/j.heares.2012.11.004
  57. C. Y. Lee, “Identifying isolated, multispeaker Mandarin tones from brief acoustic input: A perceptual and acoustic study,” J. Acoust. Soc. Am., vol. 125, no. 2, pp. 1125–1137, 2009. doi: 10.1121/1.3050322
  58. C. Zhang, and J. H. Hansen, “Phonetic variability constrained bottleneck features for joint speaker recognition and physical task stress detection,” J. Acoust. Soc. Am., vol. 148, no. 5, pp. 2912–2921, 2020. doi: 10.1121/10.0002455
  59. V. Martel-Sauvageau et al., “The impact of clear speech on the perceptual and acoustic properties of fricative–vowel sequences in speakers with dysarthria,” Amer. J. Speech Lang. Pathol., vol. 30, no. 3S, pp. 1410–1428, 2021. doi: 10.1044/2021_AJSLP-20-00157
  60. O. Hazrati, H. Ali, J. H. Hansen, and E. Tobey, “Evaluation and analysis of whispered speech for cochlear implant users: Gender identification and intelligibility,” J. Acoust. Soc. Am., vol. 138, no. 1, pp. 74–79, 2015. doi: 10.1121/1.4922230
  61. L. Marchegiani and X. Fafoutis, “On cross-language consonant identification in second language noise,” J. Acoust. Soc. Am., vol. 138, no. 4, pp. 2206–2209, 2015. doi: 10.1121/1.4930955
  62. S. Singh, S. Kumar, and B. K. Tripathi, “A comprehensive analysis of quaternion deep neural networks: architectures, applications, challenges, and future scope,” Arch. Comput. Methods Eng., pp. 1–28, 2024. doi:10.1007/s11831-024-10216-1
  63. H. Luo, A. Boemio, M. Gordon, and D. Poeppel, “The perception of FM sweeps by Chinese and English listeners,” Hearing Res., vol. 224, no. 1–2, pp. 75–83, 2007. doi: 10.1016/j.heares.2006.11.007
  64. V. Aglieri, B. Cagna, L. Velly, S. Takerkart, and P. Belin, “FMRI-based identity classification accuracy in left temporal and frontal regions predicts speaker recognition performance,” Sci. Rep., vol. 11, no. 1, p. 489, 2021. doi: 10.1038/s41598-020-79922-7
  65. V. Von Kriegstein et al., “How the human brain recognizes speech in the context of changing speakers,” J. Neurosci., vol. 30, no. 2, pp. 629–638, 2010. doi: 10.1523/JNEUROSCI.2742-09.2010
  66. X. Jiang, K. Gossack-Keenan, and M. D. Pell, “To believe or not to believe? How voice and accent information in speech alter listener impressions of trust,” Q. J. Exp. Psychol., vol. 73, no. 1, pp. 55–79, 2020. doi: 10.1177/1747021819865833
  67. J. Kaur, A. Singh, and V. Kadyan, “Automatic speech recognition system for tonal languages: State-of-the-art survey,” Arch. Comput. Methods Eng., vol. 28, no. 3, pp. 1039–1068, 2021. doi: 10.1007/s11831-020-09414-4
  68. J. Plante-Hébert, V. Boucher, and B. Jemel, “The processing of intimately familiar and unfamiliar voices: Specific neural responses of speaker recognition and identification,” PLoS One, vol. 16, no. 4, p. e0250214, 2021. doi: 10.1371/journal.pone.0250214
  69. E. Jacewicz, and R. A. Fox, “The effects of cross-generational and cross-dialectal variation on vowel identification and classification,” J. Acoust. Soc. Am., vol. 131, no. 2, pp. 1413–1433, 2012. doi:10.1121/1.3676603
  70. Y. Park et al., “Categorization in the perception of breathy voice quality and its relation to voice production in healthy speakers,” J. Speech Lang. Hear. Res., vol. 62, no. 10, pp. 3655–3666, 2019. doi:10.1044/2019_JSLHR-S-19-0048
  71. V. Iliadou et al., “Bi-syllabic, Modern Greek word lists for use in word recognition tests,” Int. J. Audio., vol. 45, no. 2, pp. 74–82, 2006. doi: 10.1080/14992020500376529
  72. S. P. Yadav, S. Zaidi, A. Mishra, and V. Yadav, “Survey on machine learning in speech emotion recognition and vision systems using a recurrent neural network (RNN),” Arch. Comput. Methods Eng., vol. 29, no. 3, pp. 1753–1770, 2022. doi: 10.1007/s11831-021-09647-x
  73. M. K. Singh et al., “Improving Speech Quality Using Deep Neural Network-Based Manipulation of Cepstral Excitation,” in Recent Developments in Electronics and Communication Systems, IOS Press, pp. 340–346, 2023. doi: 10.3233/ATDE221279
  74. K. Richardson, J. E. Sussman, E. T. Stathopoulos, and J. E. Huber, “The effect of increased vocal intensity on interarticulator timing in speakers with Parkinson's disease: A preliminary analysis,” J. Commun. Disord., vol. 52, pp. 44–64, 2014. doi: 10.1016/j.jcomdis.2014.09.004
  75. O. Amir, M. Engel, E. Shabtai, and N. Amir, “Identification of children’s gender and age by listeners,” J. Voice, vol. 26, no. 3, pp. 313–321, 2012. doi: 10.1016/j.jvoice.2011.06.001
  76. C. F. Stewart, I. Kling, and A. D’Agosto, “Modal register, vocal fry, and uptalk: Identification and perceptual judgments of inexperienced listeners,” J. Voice, 2024. doi: 10.1016/j.jvoice.2024.02.028
  77. R. Toppo, and S. Sinha, “The acoustics of gender in Indian English: Toward forensic profiling in a multilingual context,” J. Voice, 2023. doi: 10.1016/j.jvoice.2023.07.030