Phonemes: An Explanatory Study Applied to Identify a Speaker

  • Saritha KinkiriEmail author
  • Basel Barakat
  • Simeon Keates
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1241)


Speaker Identification (SI) is a process of identifying a speaker automatically via a machine using the speaker’s voice. In SI, one speaker’s voice is compared with n- number of speakers’ templates within the reference database to find the best match among the potential speakers. Speakers are capable of changing their voice, though, such as their accent, which makes is more challenging to identify who is talking. In this paper, we extracted phonemes from a speaker’s voice recording and investigated the associated frequencies and amplitudes to be assist in identifying the person who is speaking. This paper demonstrates the importance of phonemes in both speech and voice recognition systems. The results demonstrate that we can use phonemes to help the machine identify a particular speaker, however, phonemes get better accuracy in speech recognition than speaker identification.


Accent Human speech Phonemes and speaker identification 


  1. 1.
    Bazyar, M., Sudirman, R.: A new speaker change detection method in a speaker identification system for two-speakers segmentation. In: 2014 IEEE Symposium on Computer Applications and Industrial Electronics (ISCAIE), Penang, pp. 141–145 (2014)Google Scholar
  2. 2.
    Chowdhury, F.R., Selouani, S., O’Shaughnessy, D.: Distributed automatic text-independent speaker identification using GMM-UBM speaker models. In: 2009 Canadian Conference on Electrical and Computer Engineering, St. John’s, NL, pp. 372–375 (2009)Google Scholar
  3. 3.
    Nagaraja, B.G., Jayanna, H.S.: Efficient window for monolingual and cross lingual speaker identification using MFCC. In: 2013 International Conference on Advanced Computing and Communication Systems, Coimbatore, pp. 1–4 (2013)Google Scholar
  4. 4.
    Al-Hattami, A.: A phonetic and phonological study of the consonants of English and Arabic. Lang. India 10, 242–365 (2010)Google Scholar
  5. 5.
    Bacha, S., Ghozi, R., Jaidane, M., Gouider-Khoujia, N.: Arabic adaption of phonology and memory test using entropy-based analysis of word complexity. In: 2012 11th International Conference on Information Science, Signal Processing and their Applications, (ISSPA), Montreal, QC, pp. 672–677 (2012)Google Scholar
  6. 6.
    Ngo, G.H., Nguyen, M., Chen, N.F.: Phonology-augmented statistical framework for machine transliteration using limited linguistic resources. IEEE/ACM Trans. Audio Speech Lang. Process. 27(1), 192–211 (2019)CrossRefGoogle Scholar
  7. 7.
    Shih, S.S., Inkelas, S.: Auto segmental aims in surface-optimizing phonology. Linguist. J. 50(1), 137–196 (2018)Google Scholar
  8. 8.
    Uma Maheswari, N., Kabilan, A.P., Venkatesh, R.: Speaker independent speech recognition system based on phoneme identification. In: 2008 International Conference on Computing, Communication and Networking, St. Thomas, VI, pp. 1–6 (2008)Google Scholar
  9. 9.
    Rashid, R.A., Mahalin, N.H., Sarijari, M.A., Abdul Aziz, A.A.: Security system using biometric technology: design and implementation of voice recognition system (VRS). In: 2008 International Conference on Computer and Communication Engineering, Kuala Lumpur, pp. 898–902 (2008)Google Scholar
  10. 10.
    Akhila, K.S., Kumaraswamy, R.: Comparative analysis of Kannada phoneme recognition using different classifies. In: 2015 International Conference on Trends in Automation, Communications and Computing Technology (I-TACT 2015), Bangalore, pp. 1–6 (2015)Google Scholar
  11. 11.
    Panda, S.P.: Automated speech recognition system in advancement of human-computer interaction. In: 2017 International Conference on Computing Methodologies and Communication (ICCMC), Erode, pp. 302–306 (2017)Google Scholar
  12. 12.
    Xue, M., Zhu, C.: A study and application on machine learning of artificial intelligence. In: 2009 International Joint Conference on Artificial Intelligence, pp. 272–274 (2009)Google Scholar
  13. 13.
    Zhao, C., Wang, H., Hyon, S., Wei, J., Dang, J.: Efficient feature extraction of speaker identification using phoneme mean F-ratio for Chinese. In: 2012 8th International Symposium on Chinese Spoken Language Processing, pp. 345–348 (2012)Google Scholar
  14. 14.
    Lavan, N., Burton, A.M., Scott, S.K., McGettigan, C.: Flexible voices: identity perception from variable vocal signals. Psychon. Bull. Rev. J. 26(1), 90–102 (2019)CrossRefGoogle Scholar
  15. 15.
    Kinkiri, S., Keates, S.: Identification of a speaker from familiar and unfamiliar voices. In: 2019 5th International Conference on Robotics and Artificial, pp. 94–97 (2019)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.University of GreenwichChathamUK
  2. 2.Edinburgh Napier UniversityEdinburghUK

Personalised recommendations