Penerapan Hidden Markov Model (HMM) pada Pengenalan Penutur

  • Mukhlisa Mukhlisa STMIK GI MDP
  • Maryati Gultom STMIK GI MDP
  • Derry Alamsyah STMIK GI MDP
Keywords: Speaker Recognition, Mel Frequency Cepstrum Coefficients (MFCC), Hidden Markov Model (HMM)

Abstract

Human voice depends on the position or shape of the cavity owned, so the character of the sound that each person is unique and became his identity. Identification of speakers (speaker recognition) is the process of identifying who is talking on the information contained in the speech wave. Identification of speakers can be used as attendance systems, security, and so on. Speaker recognition system in this study formed through two main processes of training (training) and recognition (recognition), where Mel Frequency Cepstrum Coefficients (MFCC) are used for feature extraction, then the model is formed based Hidden Markov sound model (HMM). The results showed that the test in real time using a microphone accuracy rate of 30%. While testing of the recording file 100%. The level of accuracy depends heavily on the ability of clustering and classification.

References

[1] AbdallahS, Osman I and Mustafa M.Text-Independent Speaker Identification Using Hidden Markov Model. 2012.World of Computer Science and Information
Technology Jurnal, Vol. 2, No. 6.
[2] Wang, Jun, dkk. Sequential Model Adaptation for Speaker Verification. 2008. INTERSPEECH.
[3] Vyawahare, S.S. Speaker Recognition. 2013. International Journal of Engineering Research & Technology, Vol. 2.
[4] Holmes, J. And Holmes, W. Speech Synthesis and Recognition. 2001. Taylor and Francis, London.
[5] Nilsson, M and Ejnarson , Speech Recognition using Hidden Markov Model, Thesis Blekinge Institute of Technology, Sewen, 2002.
[6] Prasetyo, Eko. Data Mining Konsep dan Aplikasi Menggunakan MATLAB. 2012. Andi Offset. ISBN: 978-979-29-3282-9.
[7] Rabiner, L.R. A Tutorial in Hidden Markov Models and Selected Applications in Speech Recognition.Proceedings of IEEE 1989, Vol. 77, No. 2.
[8] Li, Xiaolin, dkk. Training Hidden Markov Model With Multiple Observation A Combinatorial Method. Proceedings of IEEE 2000, Vol. 22, No. 4.
[9] Irawan, F.A. Buku Pintar Pemrograman MATLAB. MediaKom.2012. ISBN: 978-979-877- 273-3.
[10] Simarmata, Janner. Rekayasa Perangkat Lunak. Andi Offset. 2010. ISBN: 978-979-29- 1347-7
Published
2014-09-11